Overview

The main goal here is to help the learners be comfortable with the RStudio interface. - Go very slowly in the “Getting set up” section. Make sure everyone is following along (remind learners to use the stickies). Plan with the helpers at this point to go around the room, and be available to help. It’s important to make sure that learners are in the correct working directory, and that they create a data (all lowercase) subfolder.

objectives

questions

What is R? What is RStudio?

The term “R” is used to refer to both the programming language and the software that interprets the scripts written using it.

RStudio is currently a very popular way to not only write your R scripts but also to interact with the R software. To function correctly, RStudio needs R and therefore both need to be installed on your computer.

To make it easier to interact with R, we will use RStudio. RStudio is the most popular IDE (Integrated Development Environment) for R. An IDE is a piece of software that provides tools to make programming easier.

You can also use the R Presentations feature to present your work in an HTML5 presentation mixing Markdown and R code. You can display these within R Studio or your browser. There are many options for customising your presentation slides, including an option for showing LaTeX equations. This can help you collaborate with others and also has an application in teaching and classroom use.

Why learn R?

R does not involve lots of pointing and clicking, and that’s a good thing

The learning curve might be steeper than with other software but with R, the results of your analysis do not rely on remembering a succession of pointing and clicking, but instead on a series of written commands, and that’s a good thing! So, if you want to redo your analysis because you collected more data, you don’t have to remember which button you clicked in which order to obtain your results; you just have to run your script again.

Working with scripts makes the steps you used in your analysis clear, and the code you write can be inspected by someone else who can give you feedback and spot mistakes.

Working with scripts forces you to have a deeper understanding of what you are doing, and facilitates your learning and comprehension of the methods you use.

R code is great for reproducibility

Reproducibility is when someone else (including your future self) can obtain the same results from the same dataset when using the same analysis.

R integrates with other tools to generate manuscripts from your code. If you collect more data, or fix a mistake in your dataset, the figures and the statistical tests in your manuscript are updated automatically.

An increasing number of journals and funding agencies expect analyses to be reproducible, so knowing R will give you an edge with these requirements.

To further support reproducibility and transparency, there are also packages that help you with dependency management: keeping track of which packages we are loading and how they depend on the package version you are using. This helps you make sure existing workflows work consistently and continue doing what they did before.

Packages like renv let you “save” and “load” the state of your project library, also keeping track of the package version you use and the source it can be retrieved from.

R is interdisciplinary and extensible

With 10,000+ packages that can be installed to extend its capabilities, R provides a framework that allows you to combine statistical approaches from many scientific disciplines to best suit the analytical framework you need to analyze your data. For instance, R has packages for image analysis, GIS, time series, population genetics, and a lot more.

R works on data of all shapes and sizes

The skills you learn with R scale easily with the size of your dataset. Whether your dataset has hundreds or millions of lines, it won’t make much difference to you.

R is designed for data analysis. It comes with special data structures and data types that make handling of missing data and statistical factors convenient.

R can connect to spreadsheets, databases, and many other data formats, on your computer or on the web.

RStudio extends what R can do, and makes it easier to write R code and interact with R.

A tour of RStudio

Knowing your way around RStudio

Let’s start by learning about RStudio, which is an Integrated Development Environment (IDE) for working with R.

The RStudio IDE open-source product is free under the Affero General Public License (AGPL) v3. The RStudio IDE is also available with a commercial license and priority email support from RStudio, Inc.

We will use the RStudio IDE to write code, navigate the files on our computer, inspect the variables we create, and visualize the plots we generate. RStudio can also be used for other things (e.g., version control, developing packages, writing Shiny apps) that we will not cover during the workshop.

One of the advantages of using RStudio is that all the information you need to write code is available in a single window. Additionally, RStudio provides many shortcuts, autocompletion, and highlighting for the major file types you use while developing in R. RStudio makes typing easier and less error-prone.

Getting set up

It is good practice to keep a set of related data, analyses, and text self-contained in a single folder called the working directory. All of the scripts within this folder can then use relative paths to files. Relative paths indicate where inside the project a file is located (as opposed to absolute paths, which point to where a file is on a specific computer). Working this way makes it a lot easier to move your project around on your computer and share it with others without having to directly modify file paths in the individual scripts.

RStudio provides a helpful set of tools to do this through its “Projects” interface, which not only creates a working directory for you but also remembers its location (allowing you to quickly navigate to it). The interface also (optionally) preserves custom settings and open files to make it easier to resume work after a break.

Create a new project

  • Under the File menu, click on New project, choose New directory, then New project
  • Enter a name for this new folder (or “directory”) and choose a convenient location for it. This will be your working directory for the rest of the day (e.g., ~/data-carpentry)
  • Click on Create project
  • Create a new file where we will type our scripts. Go to File > New File > R script. Click the save icon on your toolbar and save your script as “script.R”.

The simplest way to open an RStudio project once it has been created is to navigate through your files to where the project was saved and double click on the .Rproj (blue cube) file. This will open RStudio and start your R session in the same directory as the .Rproj file. All your data, plots and scripts will now be relative to the project directory. RStudio projects have the added benefit of allowing you to open multiple projects at the same time each open to its own project directory. This allows you to keep multiple projects open without them interfering with each other.

The RStudio Interface

Let’s take a quick tour of RStudio.

Screenshot of Packages pane

RStudio is divided into four “panes”. The placement of these panes and their content can be customized (see menu, Tools -> Global Options -> Pane Layout).

The Default Layout is:

  • Top Left - Source: your scripts and documents
  • Bottom Left - Console: what R would look and be like without RStudio
  • Top Right - Environment/History: look here to see what you have done
  • Bottom Right - Files and more: see the contents of the project/working directory here, like your Script.R file

Organizing your working directory

Using a consistent folder structure across your projects will help keep things organized and make it easy to find/file things in the future. This can be especially helpful when you have multiple projects. In general, you might create directories (folders) for scripts, data, and documents. Here are some examples of suggested directories:

  • data/ Use this folder to store your raw data and intermediate datasets. For the sake of transparency and provenance, you should always keep a copy of your raw data accessible and do as much of your data cleanup and preprocessing programmatically (i.e., with scripts, rather than manually) as possible.
  • data_output/ When you need to modify your raw data, it might be useful to store the modified versions of the datasets in a different folder.
  • documents/ Used for outlines, drafts, and other text.
  • fig_output/ This folder can store the graphics that are generated by your scripts.
  • scripts/ A place to keep your R scripts for different analyses or plotting.

You may want additional directories or subdirectories depending on your project needs, but these should form the backbone of your working directory.

Screenshot of Packages pane

The working directory

The working directory is an important concept to understand. It is the place where R will look for and save files. When you write code for your project, your scripts should refer to files in relation to the root of your working directory and only to files within this structure.

Using RStudio projects makes this easy and ensures that your working directory is set up properly. If you need to check it, you can use getwd(). If for some reason your working directory is not the same as the location of your RStudio project, it is likely that you opened an R script or RMarkdown file not your .Rproj file. You should close out of RStudio and open the .Rproj file by double clicking on the blue cube! If you ever need to modify your working directory in a script, setwd('my/path') changes the working directory. This should be used with caution since it makes analyses hard to share across devices and with other users.

Downloading the data and getting set up

For this lesson we will use the following folders in our working directory: data/, data_output/ and fig_output/. Let’s write them all in lowercase to be consistent. We can create them using the RStudio interface by clicking on the “New Folder” button in the file pane (bottom right), or directly from R by typing at console:

dir.create("data")
dir.create("data_output")
dir.create("fig_output")

You can either download the data used for this lesson from GitHub or with R. You can copy the data from this GitHub link and paste it into a file called SAFI_clean.csv in the data/ directory you just created. Or you can do this directly from R by copying and pasting this in your terminal (your instructor can place this chunk of code in the Etherpad):

("C:/Users/Francis Muoghalu/Documents/vl_data_review_2024/data/SAFI_clean.csv")

Interacting with R

The basis of programming is that we write down instructions for the computer to follow, and then we tell the computer to follow those instructions. We write, or code, instructions in R because it is a common language that both the computer and we can understand. We call the instructions commands and we tell the computer to follow the instructions by executing (also called running) those commands.

There are two main ways of interacting with R: by using the console or by using script files (plain text files that contain your code). The console pane (in RStudio, the bottom left panel) is the place where commands written in the R language can be typed and executed immediately by the computer. It is also where the results will be shown for commands that have been executed. You can type commands directly into the console and press Enter to execute those commands, but they will be forgotten when you close the session.

Because we want our code and workflow to be reproducible, it is better to type the commands we want in the script editor and save the script. This way, there is a complete record of what we did, and anyone (including our future selves!) can easily replicate the results on their computer.

RStudio allows you to execute commands directly from the script editor by using the Ctrl + Enter shortcut (on Mac, Cmd + Return will work). The command on the current line in the script (indicated by the cursor) or all of the commands in selected text will be sent to the console and executed when you press Ctrl + Enter. If there is information in the console you do not need anymore, you can clear it with Ctrl + L. You can find other keyboard shortcuts in this RStudio cheatsheet about the RStudio IDE.

At some point in your analysis, you may want to check the content of a variable or the structure of an object without necessarily keeping a record of it in your script. You can type these commands and execute them directly in the console. RStudio provides the Ctrl + 1 and Ctrl + 2 shortcuts allow you to jump between the script and the console panes.

If R is ready to accept commands, the R console shows a > prompt. If R receives a command (by typing, copy-pasting, or sent from the script editor using Ctrl + Enter), R will try to execute it and, when ready, will show the results and come back with a new > prompt to wait for new commands.

If R is still waiting for you to enter more text, the console will show a + prompt. It means that you haven’t finished entering a complete command. This is likely because you have not ‘closed’ a parenthesis or quotation, i.e. you don’t have the same number of left-parentheses as right-parentheses or the same number of opening and closing quotation marks. When this happens, and you thought you finished typing your command, click inside the console window and press Esc; this will cancel the incomplete command and return you to the > prompt. You can then proofread the command(s) you entered and correct the error.

Installing additional packages using the packages tab

In addition to the core R installation, there are in excess of 10,000 additional packages which can be used to extend the functionality of R. Many of these have been written by R users and have been made available in central repositories, like the one hosted at CRAN, for anyone to download and install into their own R environment. You should have already installed the packages ‘ggplot2’ and ’dplyr. If you have not, please do so now using these instructions.

You can see if you have a package installed by looking in the packages tab (on the lower-right by default). You can also type the command installed.packages() into the console and examine the output.

Screenshot of Packages pane

Additional packages can be installed from the ‘packages’ tab. On the packages tab, click the ‘Install’ icon and start typing the name of the package you want in the text box. As you type, packages matching your starting characters will be displayed in a drop-down list so that you can select them.

Screenshot of Packages pane

At the bottom of the Install Packages window is a check box to ‘Install’ dependencies. This is ticked by default, which is usually what you want. Packages can (and do) make use of functionality built into other packages, so for the functionality contained in the package you are installing to work properly, there may be other packages which have to be installed with them. The ‘Install dependencies’ option makes sure that this happens.

Exercise

Use both the Console and the Packages tab to confirm that you have the tidyverse installed.

Solution

Scroll through packages tab down to ‘tidyverse’. You can also type a few characters into the searchbox. The ‘tidyverse’ package is really a package of packages, including ‘ggplot2’ and ‘dplyr’, both of which require other packages to run correctly. All of these packages will be installed automatically. Depending on what packages have previously been installed in your R environment, the install of ‘tidyverse’ could be very quick or could take several minutes. As the install proceeds, messages relating to its progress will be written to the console. You will be able to see all of the packages which are actually being installed.

Because the install process accesses the CRAN repository, you will need an Internet connection to install packages.

It is also possible to install packages from other repositories, as well as Github or the local file system, but we won’t be looking at these options in this lesson.

Installing additional packages using R code

If you were watching the console window when you started the install of ‘tidyverse’, you may have noticed that the line

install.packages("tidyverse")

was written to the console before the start of the installation messages.

You could also have installed the tidyverse packages by running this command directly at the R terminal.

We will be using another package called here throughout the workshop to manage paths and directories. We will discuss it more detail in a later episode, but we will install it now in the console:

install.packages("here")

keypoints

  • Use RStudio to write and run R programs.
  • Use install.packages() to install packages (libraries).

Introduction to R

The main goal is to introduce users to the various objects in R, from atomic types to creating your own objects. - While this epsiode is foundational, be careful not to get caught in the weeds as the variety of types and operations can be overwhelming for new users, especially before they understand how this fits into their own “workflow.”

objectives

  • Define the following terms as they relate to R: object, assign, call, function, arguments, options.
  • Assign values to names in R.
  • Learn how to name objects.
  • Use comments to inform script.
  • Solve simple arithmetic operations in R.
  • Call functions and use arguments to change their default options.
  • Inspect the content of vectors and manipulate their content.
  • Subset values from vectors.
  • Analyze vectors with missing data.

questions

Creating objects in R

You can get output from R simply by typing math in the console:

3 + 5
## [1] 8
12 / 7
## [1] 1.714286

Everything that exists in R is an objects: from simple numerical values, to strings, to more complex objects like vectors, matrices, and lists. Even expressions and functions are objects in R.

However, to do useful and interesting things, we need to name objects. To do so, we need to give a name followed by the assignment operator <-, and the object we want to be named:

area_hectares <- 1.0

<- is the assignment operator. It assigns values (objects) on the right to names (also called symbols) on the left. So, after executing x <- 3, the value of x is 3. The arrow can be read as 3 goes into x. For historical reasons, you can also use = for assignments, but not in every context. Because of the slight differences in syntax, it is good practice to always use <- for assignments. More generally we prefer the <- syntax over = because it makes it clear what direction the assignment is operating (left assignment), and it increases the read-ability of the code.

In RStudio, typing Alt + - (push Alt at the same time as the - key) will write <- in a single keystroke in a PC, while typing Option + - (push Option at the same time as the - key) does the same in a Mac.

Objects can be given any name such as x, current_temperature, or subject_id. You want your object names to be explicit and not too long. They cannot start with a number (2x is not valid, but x2 is). R is case sensitive (e.g., age is different from Age). There are some names that cannot be used because they are the names of fundamental objects in R (e.g., if, else, for, see here for a complete list). In general, even if it’s allowed, it’s best to not use them (e.g., c, T, mean, data, df, weights). If in doubt, check the help to see if the name is already in use. It’s also best to avoid dots (.) within an object name as in my.dataset. There are many objects in R with dots in their names for historical reasons, but because dots have a special meaning in R (for methods) and other programming languages, it’s best to avoid them. The recommended writing style is called snake_case, which implies using only lowercaseletters and numbers and separating each word with underscores (e.g., animals_weight, average_income). It is also recommended to use nouns for object names, and verbs for function names. It’s important to be consistent in the styling of your code (where you put spaces, how you name objects, etc.). Using a consistent coding style makes your code clearer to read for your future self and your collaborators. In R, three popular style guides are Google’s, Jean Fan’s and the tidyverse’s. The tidyverse’s is very comprehensive and may seem overwhelming at first. You can install the lintr package to automatically check for issues in the styling of your code.

Objects vs. variables

The naming of objects in R is somehow related to variables in many other programming languages. In many programming languages, a variable has three aspects: a name, a memory location, and the current value stored in this location. R abstracts from modifiable memory locations. In R we only have objects which cn be named. Depending on the context, name (of an object) and variable can have drastically different meanings. However, in this lesson, the two words are used synonymously. For more information see: https://cran.r-project.org/doc/manuals/r-release/R-lang.html#Objects

When assigning an value to a name, R does not print anything. You can force R to print the value by using parentheses or by typing the object name:

area_hectares <- 1.0    # doesn't print anything
(area_hectares <- 1.0)  # putting parenthesis around the call prints the value of `area_hectares`
## [1] 1
area_hectares         # and so does typing the name of the object
## [1] 1

Now that R has area_hectares in memory, we can do arithmetic with it. For instance, we may want to convert this area into acres (area in acres is 2.47 times the area in hectares):

2.47 * area_hectares
## [1] 2.47

We can also change an the value assigned to an name by assigning it a new one:

area_hectares <- 2.5
2.47 * area_hectares
## [1] 6.175

This means that assigning a value to one name does not change the values of other names. For example, let’s name the plot’s area in acres area_acres:

area_acres <- 2.47 * area_hectares

and then change (reassign) area_hectares to 50.

area_hectares <- 50

Exercise

What do you think is the current value of area_acres? 123.5 or 6.175?

Solution

The value of area_acres is still 6.175 because you have not re-run the line area_acres <- 2.47 * area_hectares since changing the value of area_hectares.

Comments

All programming languages allow the programmer to include comments in their code. Including comments to your code has many advantages: it helps you explain your reasoning and it forces you to be tidy. A commented code is also a great tool not only to your collaborators, but to your future self. Comments are the key to a reproducible analysis.

To do this in R we use the # character. Anything to the right of the # sign and up to the end of the line is treated as a comment and is ignored by R. You can start lines with comments or include them after any code on the line.

area_hectares <- 1.0            # land area in hectares
area_acres <- area_hectares * 2.47  # convert to acres
area_acres              # print land area in acres.
## [1] 2.47

RStudio makes it easy to comment or uncomment a paragraph: after selecting the lines you want to comment, press at the same time on your keyboard Ctrl + Shift + C. If you only want to comment out one line, you can put the cursor at any location of that line (i.e. no need to select the whole line), then press Ctrl + Shift + C.

Exercise

Create two variables r_length and r_width and assign them values. It should be noted that, because length is a built-in R function, R Studio might add “()” after you type length and if you leave the parentheses you will get unexpected results. This is why you might see other programmers abbreviate common words. Create a third variable r_area and give it a value based on the current values of r_length and r_width. Show that changing the values of either r_length and r_width does not affect the value of r_area.

Solution

r_length <- 2.5
r_width <- 3.2
r_area <- r_length * r_width
r_area
## [1] 8
# change the values of r_length and r_width
r_length <- 7.0
r_width <- 6.5
# the value of r_area isn't changed
r_area
## [1] 8

Functions and their arguments

Functions are “canned scripts” that automate more complicated sets of commands including operations assignments, etc. Many functions are predefined, or can be made available by importing R packages (more on that later). A function usually gets one or more inputs called arguments. Functions often (but not always) return a value. A typical example would be the function sqrt(). The input (the argument) must be a number, and the return value (in fact, the output) is the square root of that number. Executing a function (‘running it’) is called calling the function. An example of a function call is:

b <- sqrt(a)

Here, the value of a is given to the sqrt() function, the sqrt() function calculates the square root, and returns the value which is then assigned to the name b. This function is very simple, because it takes just one argument.

The return ‘value’ of a function need not be numerical (like that of sqrt()), and it also does not need to be a single item: it can be a set of things, or even a dataset. We’ll see that when we read data files into R.

Arguments can be anything, not only numbers or filenames, but also other objects. Exactly what each argument means differs per function, and must be looked up in the documentation (see below). Some functions take arguments which may either be specified by the user, or, if left out, take on a default value: these are called options. Options are typically used to alter the way the function operates, such as whether it ignores ‘bad values’, or what symbol to use in a plot. However, if you want something specific, you can specify a value of your choice which will be used instead of the default.

Let’s try a function that can take multiple arguments: round().

round(3.14159)
## [1] 3

Here, we’ve called round() with just one argument, 3.14159, and it has returned the value 3. That’s because the default is to round to the nearest whole number. If we want more digits we can see how to do that by getting information about the round function. We can use args(round) or look at the help for this function using ?round.

args(round)
## function (x, digits = 0, ...) 
## NULL
?round

We see that if we want a different number of digits, we can type digits=2 or however many we want.

round(3.14159, digits = 2)
## [1] 3.14

If you provide the arguments in the exact same order as they are defined you don’t have to name them:

round(3.14159, 2)
## [1] 3.14

And if you do name the arguments, you can switch their order:

round(digits = 2, x = 3.14159)
## [1] 3.14

It’s good practice to put the non-optional arguments (like the number you’re rounding) first in your function call, and to specify the names of all optional arguments. If you don’t, someone reading your code might have to look up the definition of a function with unfamiliar arguments to understand what you’re doing.

Exercise

Type in ?round at the console and then look at the output in the Help pane. What other functions exist that are similar to round? How do you use the digits parameter in the round function?

Vectors and data types

A vector is the most common and basic data type in R, and is pretty much the workhorse of R. A vector is composed by a series of values, which can be either numbers or characters. We can assign a series of values to a vector using the c() function. For example we can create a vector of the number of household members for the households we’ve interviewed and assign it to hh_members:

hh_members <- c(3, 7, 10, 6)
hh_members
## [1]  3  7 10  6

A vector can also contain characters. For example, we can have a vector of the building material used to construct our interview respondents’ walls (respondent_wall_type):

respondent_wall_type <- c("muddaub", "burntbricks", "sunbricks")
respondent_wall_type
## [1] "muddaub"     "burntbricks" "sunbricks"

The quotes around “muddaub”, etc. are essential here. Without the quotes R will assume there are objects called muddaub, burntbricks and sunbricks. As these names don’t exist in R’s memory, there will be an error message.

There are many functions that allow you to inspect the content of a vector. length() tells you how many elements are in a particular vector:

length(hh_members)
## [1] 4
length(respondent_wall_type)
## [1] 3

An important feature of a vector, is that all of the elements are the same type of data. The function typeof() indicates the type of an object:

typeof(hh_members)
## [1] "double"
typeof(respondent_wall_type)
## [1] "character"

The function str() provides an overview of the structure of an object and its elements. It is a useful function when working with large and complex objects:

str(hh_members)
##  num [1:4] 3 7 10 6
str(respondent_wall_type)
##  chr [1:3] "muddaub" "burntbricks" "sunbricks"

You can use the c() function to add other elements to your vector:

possessions <- c("bicycle", "radio", "television")
possessions <- c(possessions, "mobile_phone") # add to the end of the vector
possessions <- c("car", possessions) # add to the beginning of the vector
possessions
## [1] "car"          "bicycle"      "radio"        "television"   "mobile_phone"

In the first line, we take the original vector possessions, add the value "mobile_phone" to the end of it, and save the result back into possessions. Then we add the value "car" to the beginning, again saving the result back into possessions.

We can do this over and over again to grow a vector, or assemble a dataset. As we program, this may be useful to add results that we are collecting or calculating.

An atomic vector is the simplest R data type and is a linear vector of a single type. Above, we saw 2 of the 6 main atomic vector types that R uses: "character" and "numeric" (or "double"). These are the basic building blocks that all R objects are built from. The other 4 atomic vector types are:

"logical" for TRUE and FALSE (the boolean data type) "integer" for integer numbers (e.g., 2L, the L indicates to R that it’s an integer) "complex" to represent complex numbers with real and imaginary parts (e.g., 1 + 4i) and that’s all we’re going to say about them "raw" for bitstreams that we won’t discuss further

You can check the type of your vector using the typeof() function and inputting your vector as the argument.

Vectors are one of the many data structures that R uses. Other important ones are lists (list), matrices (matrix), data frames (data.frame), factors (factor) and arrays (array).

Exercise

We’ve seen that atomic vectors can be of type character, numeric (or double), integer, and logical. But what happens if we try to mix these types in a single vector?

Solution

R implicitly converts them to all be the same type.

What will happen in each of these examples? (hint: use class() to check the data type of your objects):

num_char <- c(1, 2, 3, "a")
num_logical <- c(1, 2, 3, TRUE)
char_logical <- c("a", "b", "c", TRUE)
tricky <- c(1, 2, 3, "4")

Why do you think it happens?

Solution

Vectors can be of only one data type. R tries to convert (coerce) the content of this vector to find a “common denominator” that doesn’t lose any information.

How many values in combined_logical are "TRUE" (as a character) in the following example:

num_logical <- c(1, 2, 3, TRUE)
char_logical <- c("a", "b", "c", TRUE)
combined_logical <- c(num_logical, char_logical)

Solution

Only one. There is no memory of past data types, and the coercion happens the first time the vector is evaluated. Therefore, the TRUE in num_logical gets converted into a 1 before it gets converted into "1" in combined_logical.

You’ve probably noticed that objects of different types get converted into a single, shared type within a vector. In R, we call converting objects from one class into another class coercion. These conversions happen according to a hierarchy, whereby some types get preferentially coerced into other types. Can you draw a diagram that represents the hierarchy of how these data types are coerced?

Subsetting vectors

Subsetting (sometimes referred to as extracting or indexing) involves accessing out one or more values based on their numeric placement or “index” within a vector. If we want to subset one or several values from a vector, we must provide one index or several indices in square brackets. For instance:

respondent_wall_type <- c("muddaub", "burntbricks", "sunbricks")
respondent_wall_type[2]
## [1] "burntbricks"
respondent_wall_type[c(3, 2)]
## [1] "sunbricks"   "burntbricks"

We can also repeat the indices to create an object with more elements than the original one:

more_respondent_wall_type <- respondent_wall_type[c(1, 2, 3, 2, 1, 3)]
more_respondent_wall_type
## [1] "muddaub"     "burntbricks" "sunbricks"   "burntbricks" "muddaub"    
## [6] "sunbricks"

R indices start at 1. Programming languages like Fortran, MATLAB, Julia, and R start counting at 1, because that’s what human beings typically do. Languages in the C family (including C++, Java, Perl, and Python) count from 0 because that’s simpler for computers to do.

Conditional subsetting

Another common way of subsetting is by using a logical vector. TRUE will select the element with the same index, while FALSE will not:

hh_members <- c(3, 7, 10, 6)
hh_members[c(TRUE, FALSE, TRUE, TRUE)]
## [1]  3 10  6

Typically, these logical vectors are not typed by hand, but are the output of other functions or logical tests. For instance, if you wanted to select only the values above 5:

hh_members > 5    # will return logicals with TRUE for the indices that meet the condition
## [1] FALSE  TRUE  TRUE  TRUE
## so we can use this to select only the values above 5
hh_members[hh_members > 5]
## [1]  7 10  6

You can combine multiple tests using & (both conditions are true, AND) or | (at least one of the conditions is true, OR):

hh_members[hh_members < 4 | hh_members > 7]
## [1]  3 10
hh_members[hh_members >= 4 & hh_members <= 7]
## [1] 7 6

Here, < stands for “less than”, > for “greater than”, >= for “greater than or equal to”, and == for “equal to”. The double equal sign == is a test for numerical equality between the left and right hand sides, and should not be confused with the single = sign, which performs variable assignment (similar to <-).

A common task is to search for certain strings in a vector. One could use the “or” operator | to test for equality to multiple values, but this can quickly become tedious.

possessions <- c("car", "bicycle", "radio", "television", "mobile_phone")
possessions[possessions == "car" | possessions == "bicycle"] # returns both car and bicycle
## [1] "car"     "bicycle"

The function %in% allows you to test if any of the elements of a search vector (on the left hand side) are found in the target vector (on the right hand side):

possessions %in% c("car", "bicycle")
## [1]  TRUE  TRUE FALSE FALSE FALSE

Note that the output is the same length as the search vector on the left hand side, because %in% checks whether each element of the search vector is found somewhere in the target vector. Thus, you can use %in% to select the elements in the search vector that appear in your target vector:

possessions %in% c("car", "bicycle", "motorcycle", "truck", "boat", "bus")
## [1]  TRUE  TRUE FALSE FALSE FALSE
possessions[possessions %in% c("car", "bicycle", "motorcycle", "truck", "boat", "bus")]
## [1] "car"     "bicycle"

Missing data

As R was designed to analyze datasets, it includes the concept of missing data (which is uncommon in other programming languages). Missing data are represented in vectors as NA.

When doing operations on numbers, most functions will return NA if the data you are working with include missing values. This feature makes it harder to overlook the cases where you are dealing with missing data. You can add the argument na.rm=TRUE to calculate the result while ignoring the missing values.

rooms <- c(2, 1, 1, NA, 7)
mean(rooms)
## [1] NA
max(rooms)
## [1] NA
mean(rooms, na.rm = TRUE)
## [1] 2.75
max(rooms, na.rm = TRUE)
## [1] 7

If your data include missing values, you may want to become familiar with the functions is.na(), na.omit(), and complete.cases(). See below for examples.

## Extract those elements which are not missing values.
## The ! character is also called the NOT operator
rooms[!is.na(rooms)]
## [1] 2 1 1 7
## Count the number of missing values.
## The output of is.na() is a logical vector (TRUE/FALSE equivalent to 1/0) so the sum() function here is effectively counting
sum(is.na(rooms))
## [1] 1
## Returns the object with incomplete cases removed. The returned object is an atomic vector of type `"numeric"` (or `"double"`).
na.omit(rooms)
## [1] 2 1 1 7
## attr(,"na.action")
## [1] 4
## attr(,"class")
## [1] "omit"
## Extract those elements which are complete cases. The returned object is an atomic vector of type `"numeric"` (or `"double"`).
rooms[complete.cases(rooms)]
## [1] 2 1 1 7

Recall that you can use the typeof() function to find the type of your atomic vector.

challenge

Exercise

  1. Using this vector of rooms, create a new vector with the NAs removed.
rooms <- c(1, 2, 1, 1, NA, 3, 1, 3, 2, 1, 1, 8, 3, 1, NA, 1)

  1. Use the function median() to calculate the median of the rooms vector.

  2. Use R to figure out how many households in the set use more than 2 rooms for sleeping.

Solution

rooms <- c(1, 2, 1, 1, NA, 3, 1, 3, 2, 1, 1, 8, 3, 1, NA, 1)
rooms_no_na <- rooms[!is.na(rooms)]
# or
rooms_no_na <- na.omit(rooms)
# 2.
median(rooms, na.rm = TRUE)
## [1] 1
# 3.
rooms_above_2 <- rooms_no_na[rooms_no_na > 2]
length(rooms_above_2)
## [1] 4

Now that we have learned how to write scripts, and the basics of R’s data structures, we are ready to start working with the SAFI dataset we have been using in the other lessons, and learn about data frames.

keypoints

Starting with Data

The two main goals for this lessons are:

  • To make sure that learners are comfortable with working with data frames, and can use the bracket notation to select slices/columns.
  • To expose learners to factors. Their behavior is not necessarily intuitive, and so it is important that they are guided through it the first time they are exposed to it. The content of the lesson should be enough for learners to avoid common mistakes with them.

objectives

questions

What are data frames?

Data frames are the de facto data structure for tabular data in R, and what we use for data processing, statistics, and plotting.

A data frame is the representation of data in the format of a table where the columns are vectors that all have the same length. Data frames are analogous to the more familiar spreadsheet in programs such as Excel, with one key difference. Because columns are vectors, each column must contain a single type of data (e.g., characters, integers, factors). For example, here is a figure depicting a data frame comprising a numeric, a character, and a logical vector.

A 3 by 3 data frame with columns showing numeric, character and logical values.

Data frames can be created by hand, but most commonly they are generated by the functions read_csv() or read_table(); in other words, when importing spreadsheets from your hard drive (or the web). We will now demonstrate how to import tabular data using read_csv().

Presentation of the SAFI Data

SAFI (Studying African Farmer-Led Irrigation) is a study looking at farming and irrigation methods in Tanzania and Mozambique. The survey data was collected through interviews conducted between November 2016 and June 2017. For this lesson, we will be using a subset of the available data. For information about the full teaching dataset used in other lessons in this workshop, see the dataset description.

We will be using a subset of the cleaned version of the dataset that was produced through cleaning in OpenRefine (data/SAFI_clean.csv). In this dataset, the missing data is encoded as “NULL”, each row holds information for a single interview respondent, and the columns represent:

column_name description
key_id Added to provide a unique Id for each observation. (The InstanceID field does this as well but it is not as convenient to use)
village Village name
interview_date Date of interview
no_membrs How many members in the household?
years_liv How many years have you been living in this village or neighboring village?
respondent_wall_type What type of walls does their house have (from list)
rooms How many rooms in the main house are used for sleeping?
memb_assoc Are you a member of an irrigation association?
affect_conflicts Have you been affected by conflicts with other irrigators in the area?
liv_count Number of livestock owned.
items_owned Which of the following items are owned by the household? (list)
no_meals How many meals do people in your household normally eat in a day?
months_lack_food Indicate which months, In the last 12 months have you faced a situation when you did not have enough food to feed the household?
instanceID Unique identifier for the form data submission

Importing data

You are going to load the data in R’s memory using the function read_csv() from the readr package, which is part of the tidyverse; learn more about the tidyverse collection of packages here. readr gets installed as part as the tidyverse installation. When you load the tidyverse (library(tidyverse)), the core packages (the packages used in most data analyses) get loaded, including readr.

Before proceeding, however, this is a good opportunity to talk about conflicts. Certain packages we load can end up introducing function names that are already in use by pre-loaded R packages. For instance, when we load the tidyverse package below, we will introduce two conflicting functions: filter() and lag(). This happens because filter and lag are already functions used by the stats package (already pre-loaded in R). What will happen now is that if we, for example, call the filter() function, R will use the dplyr::filter() version and not the stats::filter() one. This happens because, if conflicted, by default R uses the function from the most recently loaded package. Conflicted functions may cause you some trouble in the future, so it is important that we are aware of them so that we can properly handle them, if we want.

To do so, we just need the following functions from the conflicted package:

It is also important to know that we can, at any time, just call the function directly from the package we want, such as stats::filter().

Even with the use of an RStudio project, it can be difficult to learn how to specify paths to file locations. Enter the here package! The here package creates paths relative to the top-level directory (your RStudio project). These relative paths work regardless of where the associated source file lives inside your project, like analysis projects with data and reports in different subdirectories. This is an important contrast to using setwd(), which depends on the way you order your files on your computer.

Monsters at a fork in the road, with signs saying here, and not here. One direction, not here, leads to a scary dark forest with spiders and absolute filepaths, while the other leads to a sunny, green meadow, and a city below a rainbow and a world free of absolute filepaths. Art by Allison Horst
Image credit: Allison Horst

Before we can use the read_csv() and here() functions, we need to load the tidyverse and here packages.

Also, if you recall, the missing data is encoded as “NULL” in the dataset. We’ll tell it to the function, so R will automatically convert all the “NULL” entries in the dataset into NA.

library(tidyverse)
library(here)

interviews <- read_csv(
  here("data", "SAFI_clean.csv"), 
  na = "NULL")

In the above code, we notice the here() function takes folder and file names as inputs (e.g., "data", "SAFI_clean.csv"), each enclosed in quotations ("") and separated by a comma. The here() will accept as many names as are necessary to navigate to a particular file (e.g., here("analysis", "data", "surveys", "clean", "SAFI_clean.csv)).

The here() function can accept the folder and file names in an alternate format, using a slash (“/”) rather than commas to separate the names. The two methods are equivalent, so that here("data", "SAFI_clean.csv") and here("data/SAFI_clean.csv") produce the same result. (The slash is used on all operating systems; backslashes are not used.)

If you were to type in the code above, it is likely that the read.csv() function would appear in the automatically populated list of functions. This function is different from the read_csv() function, as it is included in the “base” packages that come pre-installed with R. Overall, read.csv() behaves similar to read_csv(), with a few notable differences. First, read.csv() coerces column names with spaces and/or special characters to different names (e.g. interview date becomes interview.date). Second, read.csv() stores data as a data.frame, where read_csv() stores data as a different kind of data frame called a tibble. We prefer tibbles because they have nice printing properties among other desirable qualities. Read more about tibbles here.

The second statement in the code above creates a data frame but doesn’t output any data because, as you might recall, assignments (<-) don’t display anything. (Note, however, that read_csv may show informational text about the data frame that is created.) If we want to check that our data has been loaded, we can see the contents of the data frame by typing its name: interviews in the console.

interviews
## Try also
## view(interviews)
## head(interviews)
## # A tibble: 131 × 14
##    key_ID village  interview_date      no_membrs years_liv respondent_wall_type
##     <dbl> <chr>    <dttm>                  <dbl>     <dbl> <chr>               
##  1      1 God      2016-11-17 00:00:00         3         4 muddaub             
##  2      2 God      2016-11-17 00:00:00         7         9 muddaub             
##  3      3 God      2016-11-17 00:00:00        10        15 burntbricks         
##  4      4 God      2016-11-17 00:00:00         7         6 burntbricks         
##  5      5 God      2016-11-17 00:00:00         7        40 burntbricks         
##  6      6 God      2016-11-17 00:00:00         3         3 muddaub             
##  7      7 God      2016-11-17 00:00:00         6        38 muddaub             
##  8      8 Chirodzo 2016-11-16 00:00:00        12        70 burntbricks         
##  9      9 Chirodzo 2016-11-16 00:00:00         8         6 burntbricks         
## 10     10 Chirodzo 2016-12-16 00:00:00        12        23 burntbricks         
## # ℹ 121 more rows
## # ℹ 8 more variables: rooms <dbl>, memb_assoc <chr>, affect_conflicts <chr>,
## #   liv_count <dbl>, items_owned <chr>, no_meals <dbl>, months_lack_food <chr>,
## #   instanceID <chr>

Note

read_csv() assumes that fields are delimited by commas. However, in several countries, the comma is used as a decimal separator and the semicolon (;) is used as a field delimiter. If you want to read in this type of files in R, you can use the read_csv2 function. It behaves exactly like read_csv but uses different parameters for the decimal and the field separators. If you are working with another format, they can be both specified by the user. Check out the help for read_csv() by typing ?read_csv to learn more. There is also the read_tsv() for tab-separated data files, and read_delim() allows you to specify more details about the structure of your file.

Note that read_csv() actually loads the data as a tibble. A tibble is an extension of R data frames used by the tidyverse. When the data is read using read_csv(), it is stored in an object of class tbl_df, tbl, and data.frame. You can see the class of an object with

class(interviews)
## [1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame"

As a tibble, the type of data included in each column is listed in an abbreviated fashion below the column names. For instance, here key_ID is a column of floating point numbers (abbreviated <dbl> for the word ‘double’), village is a column of characters (<chr>) and the interview_date is a column in the “date and time” format (<dttm>).

Inspecting data frames

When calling a tbl_df object (like interviews here), there is already a lot of information about our data frame being displayed such as the number of rows, the number of columns, the names of the columns, and as we just saw the class of data stored in each column. However, there are functions to extract this information from data frames. Here is a non-exhaustive list of some of these functions. Let’s try them out!

Size:

Content:

Names:

Summary:

Note: most of these functions are “generic.” They can be used on other types of objects besides data frames or tibbles.

Subsetting data frames

Our interviews data frame has rows and columns (it has 2 dimensions). In practice, we may not need the entire data frame; for instance, we may only be interested in a subset of the observations (the rows) or a particular set of variables (the columns). If we want to access some specific data from it, we need to specify the “coordinates” (i.e., indices) we want from it. Row numbers come first, followed by column numbers.

Tip

Subsetting a tibble with [ always results in a tibble. However, note this is not true in general for data frames, so be careful! Different ways of specifying these coordinates can lead to results with different classes. This is covered in the Software Carpentry lesson R for Reproducible Scientific Analysis.

## first element in the first column of the tibble
interviews[1, 1]
## # A tibble: 1 × 1
##   key_ID
##    <dbl>
## 1      1
## first element in the 6th column of the tibble 
interviews[1, 6]
## # A tibble: 1 × 1
##   respondent_wall_type
##   <chr>               
## 1 muddaub
## first column of the tibble (as a vector)
interviews[[1]]
##   [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18
##  [19]  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
##  [37]  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54
##  [55]  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71 127
##  [73] 133 152 153 155 178 177 180 181 182 186 187 195 196 197 198 201 202  72
##  [91]  73  76  83  85  89 101 103 102  78  80 104 105 106 109 110 113 118 125
## [109] 119 115 108 116 117 144 143 150 159 160 165 166 167 174 175 189 191 192
## [127] 126 193 194 199 200
## first column of the tibble
interviews[1]
## # A tibble: 131 × 1
##    key_ID
##     <dbl>
##  1      1
##  2      2
##  3      3
##  4      4
##  5      5
##  6      6
##  7      7
##  8      8
##  9      9
## 10     10
## # ℹ 121 more rows
## first three elements in the 7th column of the tibble
interviews[1:3, 7]
## # A tibble: 3 × 1
##   rooms
##   <dbl>
## 1     1
## 2     1
## 3     1
## the 3rd row of the tibble
interviews[3, ]
## # A tibble: 1 × 14
##   key_ID village interview_date      no_membrs years_liv respondent_wall_type
##    <dbl> <chr>   <dttm>                  <dbl>     <dbl> <chr>               
## 1      3 God     2016-11-17 00:00:00        10        15 burntbricks         
## # ℹ 8 more variables: rooms <dbl>, memb_assoc <chr>, affect_conflicts <chr>,
## #   liv_count <dbl>, items_owned <chr>, no_meals <dbl>, months_lack_food <chr>,
## #   instanceID <chr>
## equivalent to head_interviews <- head(interviews)
head_interviews <- interviews[1:6, ]

: is a special function that creates numeric vectors of integers in increasing or decreasing order, test 1:10 and 10:1 for instance.

You can also exclude certain indices of a data frame using the “-” sign:

interviews[, -1]          # The whole tibble, except the first column
## # A tibble: 131 × 13
##    village  interview_date      no_membrs years_liv respondent_wall_type rooms
##    <chr>    <dttm>                  <dbl>     <dbl> <chr>                <dbl>
##  1 God      2016-11-17 00:00:00         3         4 muddaub                  1
##  2 God      2016-11-17 00:00:00         7         9 muddaub                  1
##  3 God      2016-11-17 00:00:00        10        15 burntbricks              1
##  4 God      2016-11-17 00:00:00         7         6 burntbricks              1
##  5 God      2016-11-17 00:00:00         7        40 burntbricks              1
##  6 God      2016-11-17 00:00:00         3         3 muddaub                  1
##  7 God      2016-11-17 00:00:00         6        38 muddaub                  1
##  8 Chirodzo 2016-11-16 00:00:00        12        70 burntbricks              3
##  9 Chirodzo 2016-11-16 00:00:00         8         6 burntbricks              1
## 10 Chirodzo 2016-12-16 00:00:00        12        23 burntbricks              5
## # ℹ 121 more rows
## # ℹ 7 more variables: memb_assoc <chr>, affect_conflicts <chr>,
## #   liv_count <dbl>, items_owned <chr>, no_meals <dbl>, months_lack_food <chr>,
## #   instanceID <chr>
interviews[-c(7:131), ]   # Equivalent to head(interviews)
## # A tibble: 6 × 14
##   key_ID village interview_date      no_membrs years_liv respondent_wall_type
##    <dbl> <chr>   <dttm>                  <dbl>     <dbl> <chr>               
## 1      1 God     2016-11-17 00:00:00         3         4 muddaub             
## 2      2 God     2016-11-17 00:00:00         7         9 muddaub             
## 3      3 God     2016-11-17 00:00:00        10        15 burntbricks         
## 4      4 God     2016-11-17 00:00:00         7         6 burntbricks         
## 5      5 God     2016-11-17 00:00:00         7        40 burntbricks         
## 6      6 God     2016-11-17 00:00:00         3         3 muddaub             
## # ℹ 8 more variables: rooms <dbl>, memb_assoc <chr>, affect_conflicts <chr>,
## #   liv_count <dbl>, items_owned <chr>, no_meals <dbl>, months_lack_food <chr>,
## #   instanceID <chr>

tibbles can be subset by calling indices (as shown previously), but also by calling their column names directly:

interviews["village"]       # Result is a tibble

interviews[, "village"]     # Result is a tibble

interviews[["village"]]     # Result is a vector

interviews$village          # Result is a vector

In RStudio, you can use the autocompletion feature to get the full and correct names of the columns.

Exercise

  1. Create a tibble (interviews_100) containing only the data in row 100 of the interviews dataset.

Now, continue using interviews for each of the following activities:

  1. Notice how nrow() gave you the number of rows in the tibble?

  1. Using the number of rows in the interviews dataset that you found in question 2, extract the row that is in the middle of the dataset. Store the content of this middle row in an object named interviews_middle. (hint: This dataset has an odd number of rows, so finding the middle is a bit trickier than dividing n_rows by 2. Use the median( ) function and what you’ve learned about sequences in R to extract the middle row!

  2. Combine nrow() with the - notation above to reproduce the behavior of head(interviews), keeping just the first through 6th rows of the interviews dataset.

Solution

## 1.
interviews_100 <- interviews[100, ]
## 2.
# Saving `n_rows` to improve readability and reduce duplication
n_rows <- nrow(interviews)
interviews_last <- interviews[n_rows, ]
## 3.
interviews_middle <- interviews[median(1:n_rows), ]
## 4.
interviews_head <- interviews[-(7:n_rows), ]

#::::::::::::::::::::::::::::::::::::::::::::::::::

Factors

R has a special data class, called factor, to deal with categorical data that you may encounter when creating plots or doing statistical analyses. Factors are very useful and actually contribute to making R particularly well suited to working with data. So we are going to spend a little time introducing them.

Factors represent categorical data. They are stored as integers associated with labels and they can be ordered (ordinal) or unordered (nominal). Factors create a structured relation between the different levels (values) of a categorical variable, such as days of the week or responses to a question in a survey. This can make it easier to see how one element relates to the other elements in a column. While factors look (and often behave) like character vectors, they are actually treated as integer vectors by R. So you need to be very careful when treating them as strings.

Once created, factors can only contain a pre-defined set of values, known as levels. By default, R always sorts levels in alphabetical order. For instance, if you have a factor with 2 levels:

respondent_floor_type <- factor(c("earth", "cement", "cement", "earth"))

R will assign 1 to the level "cement" and 2 to the level "earth" (because c comes before e, even though the first element in this vector is "earth"). You can see this by using the function levels() and you can find the number of levels using nlevels():

levels(respondent_floor_type)
## [1] "cement" "earth"
nlevels(respondent_floor_type)
## [1] 2

Sometimes, the order of the factors does not matter. Other times you might want to specify the order because it is meaningful (e.g., “low”, “medium”, “high”). It may improve your visualization, or it may be required by a particular type of analysis. Here, one way to reorder our levels in the respondent_floor_type vector would be:

respondent_floor_type # current order
## [1] earth  cement cement earth 
## Levels: cement earth
respondent_floor_type <- factor(respondent_floor_type, 
                                levels = c("earth", "cement"))

respondent_floor_type # after re-ordering
## [1] earth  cement cement earth 
## Levels: earth cement

In R’s memory, these factors are represented by integers (1, 2), but are more informative than integers because factors are self describing: "cement", "earth" is more descriptive than 1, and 2. Which one is “earth”? You wouldn’t be able to tell just from the integer data. Factors, on the other hand, have this information built in. It is particularly helpful when there are many levels. It also makes renaming levels easier. Let’s say we made a mistake and need to recode “cement” to “brick”. We can do this using the fct_recode() function from the forcats package (included in the tidyverse) which provides some extra tools to work with factors.

levels(respondent_floor_type)
## [1] "earth"  "cement"
respondent_floor_type <- fct_recode(respondent_floor_type, brick = "cement")

## as an alternative, we could change the "cement" level directly using the
## levels() function, but we have to remember that "cement" is the second level
# levels(respondent_floor_type)[2] <- "brick"

levels(respondent_floor_type)
## [1] "earth" "brick"
respondent_floor_type
## [1] earth brick brick earth
## Levels: earth brick

So far, your factor is unordered, like a nominal variable. R does not know the difference between a nominal and an ordinal variable. You make your factor an ordered factor by using the ordered=TRUE option inside your factor function. Note how the reported levels changed from the unordered factor above to the ordered version below. Ordered levels use the less than sign < to denote level ranking.

respondent_floor_type_ordered <- factor(respondent_floor_type, 
                                        ordered = TRUE)

respondent_floor_type_ordered # after setting as ordered factor
## [1] earth brick brick earth
## Levels: earth < brick

Converting factors

If you need to convert a factor to a character vector, you use as.character(x).

as.character(respondent_floor_type)
## [1] "earth" "brick" "brick" "earth"

Converting factors where the levels appear as numbers (such as concentration levels, or years) to a numeric vector is a little trickier. The as.numeric() function returns the index values of the factor, not its levels, so it will result in an entirely new (and unwanted in this case) set of numbers. One method to avoid this is to convert factors to characters, and then to numbers. Another method is to use the levels() function. Compare:

year_fct <- factor(c(1990, 1983, 1977, 1998, 1990))

as.numeric(year_fct)                     # Wrong! And there is no warning...
## [1] 3 2 1 4 3
as.numeric(as.character(year_fct))       # Works...
## [1] 1990 1983 1977 1998 1990
as.numeric(levels(year_fct))[year_fct]   # The recommended way.
## [1] 1990 1983 1977 1998 1990

Notice that in the recommended levels() approach, three important steps occur:

  • We obtain all the factor levels using levels(year_fct)
  • We convert these levels to numeric values using as.numeric(levels(year_fct))
  • We then access these numeric values using the underlying integers of the vector year_fct inside the square brackets

Renaming factors

When your data is stored as a factor, you can use the plot() function to get a quick glance at the number of observations represented by each factor level. Let’s extract the memb_assoc column from our data frame, convert it into a factor, and use it to look at the number of interview respondents who were or were not members of an irrigation association:

## create a vector from the data frame column "memb_assoc"
memb_assoc <- interviews$memb_assoc

## convert it into a factor
memb_assoc <- as.factor(memb_assoc)

## let's see what it looks like
memb_assoc
##   [1] <NA> yes  <NA> <NA> <NA> <NA> no   yes  no   no   <NA> yes  no   <NA> yes 
##  [16] <NA> <NA> <NA> <NA> <NA> no   <NA> <NA> no   no   no   <NA> no   yes  <NA>
##  [31] <NA> yes  no   yes  yes  yes  <NA> yes  <NA> yes  <NA> no   no   <NA> no  
##  [46] no   yes  <NA> <NA> yes  <NA> no   yes  no   <NA> yes  no   no   <NA> no  
##  [61] yes  <NA> <NA> <NA> no   yes  no   no   no   no   yes  <NA> no   yes  <NA>
##  [76] <NA> yes  no   no   yes  no   no   yes  no   yes  no   no   <NA> yes  yes 
##  [91] yes  yes  yes  no   no   no   no   yes  no   no   yes  yes  no   <NA> no  
## [106] no   <NA> no   no   <NA> no   <NA> <NA> no   no   no   no   yes  no   no  
## [121] no   no   no   no   no   no   no   no   no   yes  <NA>
## Levels: no yes
## bar plot of the number of interview respondents who were
## members of irrigation association:
plot(memb_assoc)

Yes/no bar graph showing number of individuals who are members of irrigation association

Looking at the plot compared to the output of the vector, we can see that in addition to “no”s and “yes”s, there are some respondents for whom the information about whether they were part of an irrigation association hasn’t been recorded, and encoded as missing data. These respondents do not appear on the plot. Let’s encode them differently so they can be counted and visualized in our plot.

## Let's recreate the vector from the data frame column "memb_assoc"
memb_assoc <- interviews$memb_assoc

## replace the missing data with "undetermined"
memb_assoc[is.na(memb_assoc)] <- "undetermined"

## convert it into a factor
memb_assoc <- as.factor(memb_assoc)

## let's see what it looks like
memb_assoc
##   [1] undetermined yes          undetermined undetermined undetermined
##   [6] undetermined no           yes          no           no          
##  [11] undetermined yes          no           undetermined yes         
##  [16] undetermined undetermined undetermined undetermined undetermined
##  [21] no           undetermined undetermined no           no          
##  [26] no           undetermined no           yes          undetermined
##  [31] undetermined yes          no           yes          yes         
##  [36] yes          undetermined yes          undetermined yes         
##  [41] undetermined no           no           undetermined no          
##  [46] no           yes          undetermined undetermined yes         
##  [51] undetermined no           yes          no           undetermined
##  [56] yes          no           no           undetermined no          
##  [61] yes          undetermined undetermined undetermined no          
##  [66] yes          no           no           no           no          
##  [71] yes          undetermined no           yes          undetermined
##  [76] undetermined yes          no           no           yes         
##  [81] no           no           yes          no           yes         
##  [86] no           no           undetermined yes          yes         
##  [91] yes          yes          yes          no           no          
##  [96] no           no           yes          no           no          
## [101] yes          yes          no           undetermined no          
## [106] no           undetermined no           no           undetermined
## [111] no           undetermined undetermined no           no          
## [116] no           no           yes          no           no          
## [121] no           no           no           no           no          
## [126] no           no           no           no           yes         
## [131] undetermined
## Levels: no undetermined yes
## bar plot of the number of interview respondents who were
## members of irrigation association:
plot(memb_assoc)

Bar plot of association membership, showing missing responses.

Exercise

Solution

## Rename levels.
memb_assoc <- fct_recode(memb_assoc, No = "no",
                         Undetermined = "undetermined", Yes = "yes")
## Reorder levels. Note we need to use the new level names.
memb_assoc <- factor(memb_assoc, levels = c("No", "Yes", "Undetermined"))
plot(memb_assoc)

bar graph showing number of individuals who are members of irrigation association, including undetermined option

#::::::::::::::::::::::::::::::::::

Formatting Dates

One of the most common issues that new (and experienced!) R users have is converting date and time information into a variable that is appropriate and usable during analyses. A best practice for dealing with date data is to ensure that each component of your date is available as a separate variable. In our dataset, we have a column interview_date which contains information about the year, month, and day that the interview was conducted. Let’s convert those dates into three separate columns.

str(interviews)

We are going to use the package lubridate, , which is included in the tidyverse installation and should be loaded by default. However, if we deal with older versions of tidyverse (2022 and ealier), we can manually load it by typing library(lubridate).

If necessary, start by loading the required package:

library(lubridate)

The lubridate function ymd() takes a vector representing year, month, and day, and converts it to a Date vector. Date is a class of data recognized by R as being a date and can be manipulated as such. The argument that the function requires is flexible, but, as a best practice, is a character vector formatted as “YYYY-MM-DD”.

Let’s extract our interview_date column and inspect the structure:

dates <- interviews$interview_date
str(dates)
##  POSIXct[1:131], format: "2016-11-17" "2016-11-17" "2016-11-17" "2016-11-17" "2016-11-17" ...

When we imported the data in R, read_csv() recognized that this column contained date information. We can now use the day(), month() and year() functions to extract this information from the date, and create new columns in our data frame to store it:

interviews$day <- day(dates)
interviews$month <- month(dates)
interviews$year <- year(dates)
interviews
## # A tibble: 131 × 17
##    key_ID village  interview_date      no_membrs years_liv respondent_wall_type
##     <dbl> <chr>    <dttm>                  <dbl>     <dbl> <chr>               
##  1      1 God      2016-11-17 00:00:00         3         4 muddaub             
##  2      2 God      2016-11-17 00:00:00         7         9 muddaub             
##  3      3 God      2016-11-17 00:00:00        10        15 burntbricks         
##  4      4 God      2016-11-17 00:00:00         7         6 burntbricks         
##  5      5 God      2016-11-17 00:00:00         7        40 burntbricks         
##  6      6 God      2016-11-17 00:00:00         3         3 muddaub             
##  7      7 God      2016-11-17 00:00:00         6        38 muddaub             
##  8      8 Chirodzo 2016-11-16 00:00:00        12        70 burntbricks         
##  9      9 Chirodzo 2016-11-16 00:00:00         8         6 burntbricks         
## 10     10 Chirodzo 2016-12-16 00:00:00        12        23 burntbricks         
## # ℹ 121 more rows
## # ℹ 11 more variables: rooms <dbl>, memb_assoc <chr>, affect_conflicts <chr>,
## #   liv_count <dbl>, items_owned <chr>, no_meals <dbl>, months_lack_food <chr>,
## #   instanceID <chr>, day <int>, month <dbl>, year <dbl>

Notice the three new columns at the end of our data frame.

In our example above, the interview_date column was read in correctly as a Date variable but generally that is not the case. Date columns are often read in as character variables and one can use the as_date() function to convert them to the appropriate Date/POSIXctformat.

Let’s say we have a vector of dates in character format:

char_dates <- c("7/31/2012", "8/9/2014", "4/30/2016")
str(char_dates)
##  chr [1:3] "7/31/2012" "8/9/2014" "4/30/2016"

We can convert this vector to dates as :

as_date(char_dates, format = "%m/%d/%Y")
## [1] "2012-07-31" "2014-08-09" "2016-04-30"

Argument format tells the function the order to parse the characters and identify the month, day and year. The format above is the equivalent of mm/dd/yyyy. A wrong format can lead to parsing errors or incorrect results.

For example, observe what happens when we use a lower case y instead of upper case Y for the year.

as_date(char_dates, format = "%m/%d/%y")
## Warning: 3 failed to parse.
## [1] NA NA NA

Here, the %y part of the format stands for a two-digit year instead of a four-digit year, and this leads to parsing errors.

Or in the following example, observe what happens when the month and day elements of the format are switched.

as_date(char_dates, format = "%d/%m/%y")
## Warning: 3 failed to parse.
## [1] NA NA NA

Since there is no month numbered 30 or 31, the first and third dates cannot be parsed.

We can also use functions ymd(), mdy() or dmy() to convert character variables to date.

mdy(char_dates)
## [1] "2012-07-31" "2014-08-09" "2016-04-30"

keypoints

Data Wrangling with dplyr

  • This lesson works better if you have graphics demonstrating dplyr commands.

  • For this lesson make sure that learners are comfortable using pipes.

  • There is also sometimes some confusion on what the arguments of group_by should be, and when to use filter() and select().

objectives

  • Describe the purpose of an R package and the dplyr package.
  • Select certain columns in a dataframe with the dplyr function select.
  • Select certain rows in a dataframe according to filtering conditions with the dplyr function filter.
  • Link the output of one dplyr function to the input of another function with the ‘pipe’ operator %>%.
  • Add new columns to a dataframe that are functions of existing columns with mutate.
  • Use the split-apply-combine concept for data analysis.
  • Use summarize, group_by, and count to split a dataframe into groups of observations, apply a summary statistics for each group, and then combine the results.
  • How can I select specific rows and/or columns from a dataframe?
  • How can I combine multiple commands into a single command?
  • How can I create new columns or remove existing columns from a dataframe?

dplyr is a package for making tabular data wrangling easier by using a limited set of functions that can be combined to extract and summarize insights from your data.

Like readr, dplyr is a part of the tidyverse. These packages were loaded in R’s memory when we called library(tidyverse) earlier.

Note

The packages in the tidyverse, namely dplyr, tidyr and ggplot2 accept both the British (e.g. summarise) and American (e.g. summarize) spelling variants of different function and option names. For this lesson, we utilize the American spellings of different functions; however, feel free to use the regional variant for where you are teaching.

What is an R package?

The package dplyr provides easy tools for the most common data wrangling tasks. It is built to work directly with dataframes, with many common tasks optimized by being written in a compiled language (C++) (not all R packages are written in R!).

There are also packages available for a wide range of tasks including building plots (ggplot2, which we’ll see later), downloading data from the NCBI database, or performing statistical analysis on your data set. Many packages such as these are housed on, and downloadable from, the Comprehensive R Archive Network (CRAN) using install.packages. This function makes the package accessible by your R installation with the command library(), as you did with tidyverse earlier.

To easily access the documentation for a package within R or RStudio, use help(package = "package_name").

To learn more about dplyr after the workshop, you may want to check out this handy data transformation with dplyr cheatsheet.

Note

There are alternatives to the tidyverse packages for data wrangling, including the package data.table. See this comparison for example to get a sense of the differences between using base, tidyverse, and data.table.

::::::::::::::::::::::::::::::::::::::::::::::::::

Learning dplyr

To make sure everyone will use the same dataset for this lesson, we’ll read again the SAFI dataset that we downloaded earlier.

## load the tidyverse
library(tidyverse)
library(here)

interviews <- read_csv(here("data", "SAFI_clean.csv"), na = "NULL")

## inspect the data
interviews

## preview the data
# view(interviews)

We’re going to learn some of the most common dplyr functions:

Selecting columns and filtering rows

To select columns of a dataframe, use select(). The first argument to this function is the dataframe (interviews), and the subsequent arguments are the columns to keep, separated by commas. Alternatively, if you are selecting columns adjacent to each other, you can use a : to select a range of columns, read as “select columns from ___ to ___.” You may have done something similar in the past using subsetting. select() is essentially doing the same thing as subsetting, using a package (dplyr) instead of R’s base functions.

# to select columns throughout the dataframe
select(interviews, village, no_membrs, months_lack_food)
# to do the same thing with subsetting
interviews[c("village","no_membrs","months_lack_food")]
# to select a series of connected columns
select(interviews, village:respondent_wall_type)

To choose rows based on specific criteria, we can use the filter() function. The argument after the dataframe is the condition we want our final dataframe to adhere to (e.g. village name is Chirodzo):

# filters observations where village name is "Chirodzo"
filter(interviews, village == "Chirodzo")
## # A tibble: 39 × 14
##    key_ID village  interview_date      no_membrs years_liv respondent_wall_type
##     <dbl> <chr>    <dttm>                  <dbl>     <dbl> <chr>               
##  1      8 Chirodzo 2016-11-16 00:00:00        12        70 burntbricks         
##  2      9 Chirodzo 2016-11-16 00:00:00         8         6 burntbricks         
##  3     10 Chirodzo 2016-12-16 00:00:00        12        23 burntbricks         
##  4     34 Chirodzo 2016-11-17 00:00:00         8        18 burntbricks         
##  5     35 Chirodzo 2016-11-17 00:00:00         5        45 muddaub             
##  6     36 Chirodzo 2016-11-17 00:00:00         6        23 sunbricks           
##  7     37 Chirodzo 2016-11-17 00:00:00         3         8 burntbricks         
##  8     43 Chirodzo 2016-11-17 00:00:00         7        29 muddaub             
##  9     44 Chirodzo 2016-11-17 00:00:00         2         6 muddaub             
## 10     45 Chirodzo 2016-11-17 00:00:00         9         7 muddaub             
## # ℹ 29 more rows
## # ℹ 8 more variables: rooms <dbl>, memb_assoc <chr>, affect_conflicts <chr>,
## #   liv_count <dbl>, items_owned <chr>, no_meals <dbl>, months_lack_food <chr>,
## #   instanceID <chr>

We can also specify multiple conditions within the filter() function. We can combine conditions using either “and” or “or” statements. In an “and” statement, an observation (row) must meet every criteria to be included in the resulting dataframe. To form “and” statements within dplyr, we can pass our desired conditions as arguments in the filter() function, separated by commas:

# filters observations with "and" operator (comma)
# output dataframe satisfies ALL specified conditions
filter(interviews, village == "Chirodzo",
                   rooms > 1,
                   no_meals > 2)
## # A tibble: 10 × 14
##    key_ID village  interview_date      no_membrs years_liv respondent_wall_type
##     <dbl> <chr>    <dttm>                  <dbl>     <dbl> <chr>               
##  1     10 Chirodzo 2016-12-16 00:00:00        12        23 burntbricks         
##  2     49 Chirodzo 2016-11-16 00:00:00         6        26 burntbricks         
##  3     52 Chirodzo 2016-11-16 00:00:00        11        15 burntbricks         
##  4     56 Chirodzo 2016-11-16 00:00:00        12        23 burntbricks         
##  5     65 Chirodzo 2016-11-16 00:00:00         8        20 burntbricks         
##  6     66 Chirodzo 2016-11-16 00:00:00        10        37 burntbricks         
##  7     67 Chirodzo 2016-11-16 00:00:00         5        31 burntbricks         
##  8     68 Chirodzo 2016-11-16 00:00:00         8        52 burntbricks         
##  9    199 Chirodzo 2017-06-04 00:00:00         7        17 burntbricks         
## 10    200 Chirodzo 2017-06-04 00:00:00         8        20 burntbricks         
## # ℹ 8 more variables: rooms <dbl>, memb_assoc <chr>, affect_conflicts <chr>,
## #   liv_count <dbl>, items_owned <chr>, no_meals <dbl>, months_lack_food <chr>,
## #   instanceID <chr>

We can also form “and” statements with the & operator instead of commas:

# filters observations with "&" logical operator
# output dataframe satisfies ALL specified conditions
filter(interviews, village == "Chirodzo" &
                   rooms > 1 &
                   no_meals > 2)
## # A tibble: 10 × 14
##    key_ID village  interview_date      no_membrs years_liv respondent_wall_type
##     <dbl> <chr>    <dttm>                  <dbl>     <dbl> <chr>               
##  1     10 Chirodzo 2016-12-16 00:00:00        12        23 burntbricks         
##  2     49 Chirodzo 2016-11-16 00:00:00         6        26 burntbricks         
##  3     52 Chirodzo 2016-11-16 00:00:00        11        15 burntbricks         
##  4     56 Chirodzo 2016-11-16 00:00:00        12        23 burntbricks         
##  5     65 Chirodzo 2016-11-16 00:00:00         8        20 burntbricks         
##  6     66 Chirodzo 2016-11-16 00:00:00        10        37 burntbricks         
##  7     67 Chirodzo 2016-11-16 00:00:00         5        31 burntbricks         
##  8     68 Chirodzo 2016-11-16 00:00:00         8        52 burntbricks         
##  9    199 Chirodzo 2017-06-04 00:00:00         7        17 burntbricks         
## 10    200 Chirodzo 2017-06-04 00:00:00         8        20 burntbricks         
## # ℹ 8 more variables: rooms <dbl>, memb_assoc <chr>, affect_conflicts <chr>,
## #   liv_count <dbl>, items_owned <chr>, no_meals <dbl>, months_lack_food <chr>,
## #   instanceID <chr>

In an “or” statement, observations must meet at least one of the specified conditions. To form “or” statements we use the logical operator for “or,” which is the vertical bar (|):

# filters observations with "|" logical operator
# output dataframe satisfies AT LEAST ONE of the specified conditions
filter(interviews, village == "Chirodzo" | village == "Ruaca")
## # A tibble: 88 × 14
##    key_ID village  interview_date      no_membrs years_liv respondent_wall_type
##     <dbl> <chr>    <dttm>                  <dbl>     <dbl> <chr>               
##  1      8 Chirodzo 2016-11-16 00:00:00        12        70 burntbricks         
##  2      9 Chirodzo 2016-11-16 00:00:00         8         6 burntbricks         
##  3     10 Chirodzo 2016-12-16 00:00:00        12        23 burntbricks         
##  4     23 Ruaca    2016-11-21 00:00:00        10        20 burntbricks         
##  5     24 Ruaca    2016-11-21 00:00:00         6         4 burntbricks         
##  6     25 Ruaca    2016-11-21 00:00:00        11         6 burntbricks         
##  7     26 Ruaca    2016-11-21 00:00:00         3        20 burntbricks         
##  8     27 Ruaca    2016-11-21 00:00:00         7        36 burntbricks         
##  9     28 Ruaca    2016-11-21 00:00:00         2         2 muddaub             
## 10     29 Ruaca    2016-11-21 00:00:00         7        10 burntbricks         
## # ℹ 78 more rows
## # ℹ 8 more variables: rooms <dbl>, memb_assoc <chr>, affect_conflicts <chr>,
## #   liv_count <dbl>, items_owned <chr>, no_meals <dbl>, months_lack_food <chr>,
## #   instanceID <chr>

Pipes

What if you want to select and filter at the same time? There are three ways to do this: use intermediate steps, nested functions, or pipes.

With intermediate steps, you create a temporary dataframe and use that as input to the next function, like this:

interviews2 <- filter(interviews, village == "Chirodzo")
interviews_ch <- select(interviews2, village:respondent_wall_type)

This is readable, but can clutter up your workspace with lots of objects that you have to name individually. With multiple steps, that can be hard to keep track of.

You can also nest functions (i.e. one function inside of another), like this:

interviews_ch <- select(filter(interviews, village == "Chirodzo"),
                         village:respondent_wall_type)

This is handy, but can be difficult to read if too many functions are nested, as R evaluates the expression from the inside out (in this case, filtering, then selecting).

The last option, pipes, are a recent addition to R. Pipes let you take the output of one function and send it directly to the next, which is useful when you need to do many things to the same dataset. There are two Pipes in R: 1) %>% (called magrittr pipe; made available via the magrittr package, installed automatically with dplyr) or 2) |> (called native R pipe and it comes preinstalled with R v4.1.0 onwards). Both the pipes are, by and large, function similarly with a few differences (For more information, check: https://www.tidyverse.org/blog/2023/04/base-vs-magrittr-pipe/). The choice of which pipe to be used can be changed in the Global settings in R studio and once that is done, you can type the pipe with:

# the following example is run using magrittr pipe but the output will be same with the native pipe
interviews %>%
    filter(village == "Chirodzo") %>%
    select(village:respondent_wall_type)
## # A tibble: 39 × 5
##    village  interview_date      no_membrs years_liv respondent_wall_type
##    <chr>    <dttm>                  <dbl>     <dbl> <chr>               
##  1 Chirodzo 2016-11-16 00:00:00        12        70 burntbricks         
##  2 Chirodzo 2016-11-16 00:00:00         8         6 burntbricks         
##  3 Chirodzo 2016-12-16 00:00:00        12        23 burntbricks         
##  4 Chirodzo 2016-11-17 00:00:00         8        18 burntbricks         
##  5 Chirodzo 2016-11-17 00:00:00         5        45 muddaub             
##  6 Chirodzo 2016-11-17 00:00:00         6        23 sunbricks           
##  7 Chirodzo 2016-11-17 00:00:00         3         8 burntbricks         
##  8 Chirodzo 2016-11-17 00:00:00         7        29 muddaub             
##  9 Chirodzo 2016-11-17 00:00:00         2         6 muddaub             
## 10 Chirodzo 2016-11-17 00:00:00         9         7 muddaub             
## # ℹ 29 more rows
#interviews |>
#   filter(village == "Chirodzo") |>
#   select(village:respondent_wall_type)

In the above code, we use the pipe to send the interviews dataset first through filter() to keep rows where village is “Chirodzo”, then through select() to keep only the columns from village to respondent_wall_type. Since %>% takes the object on its left and passes it as the first argument to the function on its right, we don’t need to explicitly include the dataframe as an argument to the filter() and select() functions any more.

Some may find it helpful to read the pipe like the word “then”. For instance, in the above example, we take the dataframe interviews, then we filter for rows with village == "Chirodzo", then we select columns village:respondent_wall_type. The dplyr functions by themselves are somewhat simple, but by combining them into linear workflows with the pipe, we can accomplish more complex data wrangling operations.

If we want to create a new object with this smaller version of the data, we can assign it a new name:

interviews_ch <- interviews %>%
    filter(village == "Chirodzo") %>%
    select(village:respondent_wall_type)

interviews_ch
## # A tibble: 39 × 5
##    village  interview_date      no_membrs years_liv respondent_wall_type
##    <chr>    <dttm>                  <dbl>     <dbl> <chr>               
##  1 Chirodzo 2016-11-16 00:00:00        12        70 burntbricks         
##  2 Chirodzo 2016-11-16 00:00:00         8         6 burntbricks         
##  3 Chirodzo 2016-12-16 00:00:00        12        23 burntbricks         
##  4 Chirodzo 2016-11-17 00:00:00         8        18 burntbricks         
##  5 Chirodzo 2016-11-17 00:00:00         5        45 muddaub             
##  6 Chirodzo 2016-11-17 00:00:00         6        23 sunbricks           
##  7 Chirodzo 2016-11-17 00:00:00         3         8 burntbricks         
##  8 Chirodzo 2016-11-17 00:00:00         7        29 muddaub             
##  9 Chirodzo 2016-11-17 00:00:00         2         6 muddaub             
## 10 Chirodzo 2016-11-17 00:00:00         9         7 muddaub             
## # ℹ 29 more rows

Note that the final dataframe (interviews_ch) is the leftmost part of this expression.

Exercise

Using pipes, subset the interviews data to include interviews where respondents were members of an irrigation association (memb_assoc) and retain only the columns affect_conflicts, liv_count, and no_meals.

Solution

interviews %>%
    filter(memb_assoc == "yes") %>%
    select(affect_conflicts, liv_count, no_meals)
## # A tibble: 33 × 3
##    affect_conflicts liv_count no_meals
##    <chr>                <dbl>    <dbl>
##  1 once                     3        2
##  2 never                    2        2
##  3 never                    2        3
##  4 once                     3        2
##  5 frequently               1        3
##  6 more_once                5        2
##  7 more_once                3        2
##  8 more_once                2        3
##  9 once                     3        3
## 10 never                    3        3
## # ℹ 23 more rows

::::::::::::::::::::::::::::::::::::::::::::::::::

Mutate

Frequently you’ll want to create new columns based on the values in existing columns, for example to do unit conversions, or to find the ratio of values in two columns. For this we’ll use mutate().

We might be interested in the ratio of number of household members to rooms used for sleeping (i.e. avg number of people per room):

interviews %>%
    mutate(people_per_room = no_membrs / rooms)
## # A tibble: 131 × 15
##    key_ID village  interview_date      no_membrs years_liv respondent_wall_type
##     <dbl> <chr>    <dttm>                  <dbl>     <dbl> <chr>               
##  1      1 God      2016-11-17 00:00:00         3         4 muddaub             
##  2      2 God      2016-11-17 00:00:00         7         9 muddaub             
##  3      3 God      2016-11-17 00:00:00        10        15 burntbricks         
##  4      4 God      2016-11-17 00:00:00         7         6 burntbricks         
##  5      5 God      2016-11-17 00:00:00         7        40 burntbricks         
##  6      6 God      2016-11-17 00:00:00         3         3 muddaub             
##  7      7 God      2016-11-17 00:00:00         6        38 muddaub             
##  8      8 Chirodzo 2016-11-16 00:00:00        12        70 burntbricks         
##  9      9 Chirodzo 2016-11-16 00:00:00         8         6 burntbricks         
## 10     10 Chirodzo 2016-12-16 00:00:00        12        23 burntbricks         
## # ℹ 121 more rows
## # ℹ 9 more variables: rooms <dbl>, memb_assoc <chr>, affect_conflicts <chr>,
## #   liv_count <dbl>, items_owned <chr>, no_meals <dbl>, months_lack_food <chr>,
## #   instanceID <chr>, people_per_room <dbl>

We may be interested in investigating whether being a member of an irrigation association had any effect on the ratio of household members to rooms. To look at this relationship, we will first remove data from our dataset where the respondent didn’t answer the question of whether they were a member of an irrigation association. These cases are recorded as “NULL” in the dataset.

To remove these cases, we could insert a filter() in the chain:

interviews %>%
    filter(!is.na(memb_assoc)) %>%
    mutate(people_per_room = no_membrs / rooms)
## # A tibble: 92 × 15
##    key_ID village  interview_date      no_membrs years_liv respondent_wall_type
##     <dbl> <chr>    <dttm>                  <dbl>     <dbl> <chr>               
##  1      2 God      2016-11-17 00:00:00         7         9 muddaub             
##  2      7 God      2016-11-17 00:00:00         6        38 muddaub             
##  3      8 Chirodzo 2016-11-16 00:00:00        12        70 burntbricks         
##  4      9 Chirodzo 2016-11-16 00:00:00         8         6 burntbricks         
##  5     10 Chirodzo 2016-12-16 00:00:00        12        23 burntbricks         
##  6     12 God      2016-11-21 00:00:00         7        20 burntbricks         
##  7     13 God      2016-11-21 00:00:00         6         8 burntbricks         
##  8     15 God      2016-11-21 00:00:00         5        30 sunbricks           
##  9     21 God      2016-11-21 00:00:00         8        20 burntbricks         
## 10     24 Ruaca    2016-11-21 00:00:00         6         4 burntbricks         
## # ℹ 82 more rows
## # ℹ 9 more variables: rooms <dbl>, memb_assoc <chr>, affect_conflicts <chr>,
## #   liv_count <dbl>, items_owned <chr>, no_meals <dbl>, months_lack_food <chr>,
## #   instanceID <chr>, people_per_room <dbl>

The ! symbol negates the result of the is.na() function. Thus, if is.na() returns a value of TRUE (because the memb_assoc is missing), the ! symbol negates this and says we only want values of FALSE, where memb_assoc is not missing.

Exercise

Create a new dataframe from the interviews data that meets the following criteria: contains only the village column and a new column called total_meals containing a value that is equal to the total number of meals served in the household per day on average (no_membrs times no_meals). Only the rows where total_meals is greater than 20 should be shown in the final dataframe.

Hint: think about how the commands should be ordered to produce this data frame!

Solution

interviews_total_meals <- interviews %>%
    mutate(total_meals = no_membrs * no_meals) %>%
    filter(total_meals > 20) %>%
    select(village, total_meals)

Split-apply-combine data analysis and the summarize() function

Many data analysis tasks can be approached using the split-apply-combine paradigm: split the data into groups, apply some analysis to each group, and then combine the results. dplyr makes this very easy through the use of the group_by() function.

The summarize() function

group_by() is often used together with summarize(), which collapses each group into a single-row summary of that group. group_by() takes as arguments the column names that contain the categorical variables for which you want to calculate the summary statistics. So to compute the average household size by village:

interviews %>%
    group_by(village) %>%
    summarize(mean_no_membrs = mean(no_membrs))
## # A tibble: 3 × 2
##   village  mean_no_membrs
##   <chr>             <dbl>
## 1 Chirodzo           7.08
## 2 God                6.86
## 3 Ruaca              7.57

You may also have noticed that the output from these calls doesn’t run off the screen anymore. It’s one of the advantages of tbl_df over dataframe.

You can also group by multiple columns:

interviews %>%
    group_by(village, memb_assoc) %>%
    summarize(mean_no_membrs = mean(no_membrs))
## `summarise()` has grouped output by 'village'. You can override using the
## `.groups` argument.
## # A tibble: 9 × 3
## # Groups:   village [3]
##   village  memb_assoc mean_no_membrs
##   <chr>    <chr>               <dbl>
## 1 Chirodzo no                   8.06
## 2 Chirodzo yes                  7.82
## 3 Chirodzo <NA>                 5.08
## 4 God      no                   7.13
## 5 God      yes                  8   
## 6 God      <NA>                 6   
## 7 Ruaca    no                   7.18
## 8 Ruaca    yes                  9.5 
## 9 Ruaca    <NA>                 6.22

Note that the output is a grouped tibble. To obtain an ungrouped tibble, use the ungroup function:

interviews %>%
    group_by(village, memb_assoc) %>%
    summarize(mean_no_membrs = mean(no_membrs)) %>%
    ungroup()
## `summarise()` has grouped output by 'village'. You can override using the
## `.groups` argument.
## # A tibble: 9 × 3
##   village  memb_assoc mean_no_membrs
##   <chr>    <chr>               <dbl>
## 1 Chirodzo no                   8.06
## 2 Chirodzo yes                  7.82
## 3 Chirodzo <NA>                 5.08
## 4 God      no                   7.13
## 5 God      yes                  8   
## 6 God      <NA>                 6   
## 7 Ruaca    no                   7.18
## 8 Ruaca    yes                  9.5 
## 9 Ruaca    <NA>                 6.22

When grouping both by village and membr_assoc, we see rows in our table for respondents who did not specify whether they were a member of an irrigation association. We can exclude those data from our table using a filter step.

interviews %>%
    filter(!is.na(memb_assoc)) %>%
    group_by(village, memb_assoc) %>%
    summarize(mean_no_membrs = mean(no_membrs))
## `summarise()` has grouped output by 'village'. You can override using the
## `.groups` argument.
## # A tibble: 6 × 3
## # Groups:   village [3]
##   village  memb_assoc mean_no_membrs
##   <chr>    <chr>               <dbl>
## 1 Chirodzo no                   8.06
## 2 Chirodzo yes                  7.82
## 3 God      no                   7.13
## 4 God      yes                  8   
## 5 Ruaca    no                   7.18
## 6 Ruaca    yes                  9.5

Once the data are grouped, you can also summarize multiple variables at the same time (and not necessarily on the same variable). For instance, we could add a column indicating the minimum household size for each village for each group (members of an irrigation association vs not):

interviews %>%
    filter(!is.na(memb_assoc)) %>%
    group_by(village, memb_assoc) %>%
    summarize(mean_no_membrs = mean(no_membrs),
              min_membrs = min(no_membrs))
## `summarise()` has grouped output by 'village'. You can override using the
## `.groups` argument.
## # A tibble: 6 × 4
## # Groups:   village [3]
##   village  memb_assoc mean_no_membrs min_membrs
##   <chr>    <chr>               <dbl>      <dbl>
## 1 Chirodzo no                   8.06          4
## 2 Chirodzo yes                  7.82          2
## 3 God      no                   7.13          3
## 4 God      yes                  8             5
## 5 Ruaca    no                   7.18          2
## 6 Ruaca    yes                  9.5           5

It is sometimes useful to rearrange the result of a query to inspect the values. For instance, we can sort on min_membrs to put the group with the smallest household first:

interviews %>%
    filter(!is.na(memb_assoc)) %>%
    group_by(village, memb_assoc) %>%
    summarize(mean_no_membrs = mean(no_membrs),
              min_membrs = min(no_membrs)) %>%
    arrange(min_membrs)
## `summarise()` has grouped output by 'village'. You can override using the
## `.groups` argument.
## # A tibble: 6 × 4
## # Groups:   village [3]
##   village  memb_assoc mean_no_membrs min_membrs
##   <chr>    <chr>               <dbl>      <dbl>
## 1 Chirodzo yes                  7.82          2
## 2 Ruaca    no                   7.18          2
## 3 God      no                   7.13          3
## 4 Chirodzo no                   8.06          4
## 5 God      yes                  8             5
## 6 Ruaca    yes                  9.5           5

To sort in descending order, we need to add the desc() function. If we want to sort the results by decreasing order of minimum household size:

interviews %>%
    filter(!is.na(memb_assoc)) %>%
    group_by(village, memb_assoc) %>%
    summarize(mean_no_membrs = mean(no_membrs),
              min_membrs = min(no_membrs)) %>%
    arrange(desc(min_membrs))
## `summarise()` has grouped output by 'village'. You can override using the
## `.groups` argument.
## # A tibble: 6 × 4
## # Groups:   village [3]
##   village  memb_assoc mean_no_membrs min_membrs
##   <chr>    <chr>               <dbl>      <dbl>
## 1 God      yes                  8             5
## 2 Ruaca    yes                  9.5           5
## 3 Chirodzo no                   8.06          4
## 4 God      no                   7.13          3
## 5 Chirodzo yes                  7.82          2
## 6 Ruaca    no                   7.18          2

Counting

When working with data, we often want to know the number of observations found for each factor or combination of factors. For this task, dplyr provides count(). For example, if we wanted to count the number of rows of data for each village, we would do:

interviews %>%
    count(village)
## # A tibble: 3 × 2
##   village      n
##   <chr>    <int>
## 1 Chirodzo    39
## 2 God         43
## 3 Ruaca       49

For convenience, count() provides the sort argument to get results in decreasing order:

interviews %>%
    count(village, sort = TRUE)
## # A tibble: 3 × 2
##   village      n
##   <chr>    <int>
## 1 Ruaca       49
## 2 God         43
## 3 Chirodzo    39

Exercise

How many households in the survey have an average of two meals per day? Three meals per day? Are there any other numbers of meals represented?

Solution

interviews %>%
   count(no_meals)
## # A tibble: 2 × 2
##   no_meals     n
##      <dbl> <int>
## 1        2    52
## 2        3    79

Use group_by() and summarize() to find the mean, min, and max number of household members for each village. Also add the number of observations (hint: see ?n).

Solution

interviews %>%
  group_by(village) %>%
  summarize(
      mean_no_membrs = mean(no_membrs),
      min_no_membrs = min(no_membrs),
      max_no_membrs = max(no_membrs),
      n = n()
  )
## # A tibble: 3 × 5
##   village  mean_no_membrs min_no_membrs max_no_membrs     n
##   <chr>             <dbl>         <dbl>         <dbl> <int>
## 1 Chirodzo           7.08             2            12    39
## 2 God                6.86             3            15    43
## 3 Ruaca              7.57             2            19    49

What was the largest household interviewed in each month?

Solution

# if not already included, add month, year, and day columns
library(lubridate) # load lubridate if not already loaded
interviews %>%
    mutate(month = month(interview_date),
           day = day(interview_date),
           year = year(interview_date)) %>%
    group_by(year, month) %>%
    summarize(max_no_membrs = max(no_membrs))
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
## # A tibble: 5 × 3
## # Groups:   year [2]
##    year month max_no_membrs
##   <dbl> <dbl>         <dbl>
## 1  2016    11            19
## 2  2016    12            12
## 3  2017     4            17
## 4  2017     5            15
## 5  2017     6            15

keypoints

Data Wrangling with tidyr

objectives

  • Describe the concept of a wide and a long table format and for which purpose those formats are useful.
  • Describe the roles of variable names and their associated values when a table is reshaped.
  • Reshape a dataframe from long to wide format and back with the pivot_wider and pivot_longer commands from the tidyr package.
  • Export a dataframe to a csv file.

questions

  • How can I reformat a data frame to meet my needs?

dplyr pairs nicely with tidyr which enables you to swiftly convert between different data formats (long vs. wide) for plotting and analysis. To learn more about tidyr after the workshop, you may want to check out this handy data tidying with tidyr cheatsheet.

To make sure everyone will use the same dataset for this lesson, we’ll read again the SAFI dataset that we downloaded earlier.

## load the tidyverse
library(tidyverse)
library(here)

interviews <- read_csv(here("data", "SAFI_clean.csv"), na = "NULL")

## inspect the data
interviews

## preview the data
# view(interviews)

Reshaping with pivot_wider() and pivot_longer()

There are essentially three rules that define a “tidy” dataset:

  1. Each variable has its own column
  2. Each observation has its own row
  3. Each value must have its own cell

This graphic visually represents the three rules that define a “tidy” dataset:

Screenshot of Packages pane

R for Data Science, Wickham H and Grolemund G (https://r4ds.had.co.nz/index.html) © Wickham, Grolemund 2017 This image is licenced under Attribution-NonCommercial-NoDerivs 3.0 United States (CC-BY-NC-ND 3.0 US)

In this section we will explore how these rules are linked to the different data formats researchers are often interested in: “wide” and “long”. This tutorial will help you efficiently transform your data shape regardless of original format. First we will explore qualities of the interviews data and how they relate to these different types of data formats.

Long and wide data formats

In the interviews data, each row contains the values of variables associated with each record collected (each interview in the villages). It is stated that the key_ID was “added to provide a unique Id for each observation” and the instanceID “does this as well but it is not as convenient to use.”

Once we have established that key_ID and instanceID are both unique we can use either variable as an identifier corresponding to the 131 interview records.

interviews %>% 
  select(key_ID) %>% 
  distinct() %>% 
  count()
## # A tibble: 1 × 1
##       n
##   <int>
## 1   131
interviews %>% 
  select(instanceID) %>% 
  distinct() %>% 
  count()
## # A tibble: 1 × 1
##       n
##   <int>
## 1   131

As seen in the code below, for each interview date in each village no instanceIDs are the same. Thus, this format is what is called a “long” data format, where each observation occupies only one row in the dataframe.

interviews %>%
  filter(village == "Chirodzo") %>%
  select(key_ID, village, interview_date, instanceID) %>%
  sample_n(size = 10)
## # A tibble: 10 × 4
##    key_ID village  interview_date      instanceID                               
##     <dbl> <chr>    <dttm>              <chr>                                    
##  1     66 Chirodzo 2016-11-16 00:00:00 uuid:a457eab8-971b-4417-a971-2e55b8702816
##  2     47 Chirodzo 2016-11-17 00:00:00 uuid:2d0b1936-4f82-4ec3-a3b5-7c3c8cd6cc2b
##  3     50 Chirodzo 2016-11-16 00:00:00 uuid:4267c33c-53a7-46d9-8bd6-b96f58a4f92c
##  4     51 Chirodzo 2016-11-16 00:00:00 uuid:18ac8e77-bdaf-47ab-85a2-e4c947c9d3ce
##  5     55 Chirodzo 2016-11-16 00:00:00 uuid:883c0433-9891-4121-bc63-744f082c1fa0
##  6     61 Chirodzo 2016-11-16 00:00:00 uuid:2401cf50-8859-44d9-bd14-1bf9128766f2
##  7     57 Chirodzo 2016-11-16 00:00:00 uuid:a7184e55-0615-492d-9835-8f44f3b03a71
##  8      8 Chirodzo 2016-11-16 00:00:00 uuid:d6cee930-7be1-4fd9-88c0-82a08f90fb5a
##  9     60 Chirodzo 2016-11-16 00:00:00 uuid:85465caf-23e4-4283-bb72-a0ef30e30176
## 10     48 Chirodzo 2016-11-16 00:00:00 uuid:e180899c-7614-49eb-a97c-40ed013a38a2

We notice that the layout or format of the interviews data is in a format that adheres to rules 1-3, where

  • each column is a variable
  • each row is an observation
  • each value has its own cell

This is called a “long” data format. But, we notice that each column represents a different variable. In the “longest” data format there would only be three columns, one for the id variable, one for the observed variable, and one for the observed value (of that variable). This data format is quite unsightly and difficult to work with, so you will rarely see it in use.

Alternatively, in a “wide” data format we see modifications to rule 1, where each column no longer represents a single variable. Instead, columns can represent different levels/values of a variable. For instance, in some data you encounter the researchers may have chosen for every survey date to be a different column.

These may sound like dramatically different data layouts, but there are some tools that make transitions between these layouts much simpler than you might think! The gif below shows how these two formats relate to each other, and gives you an idea of how we can use R to shift from one format to the other.

Screenshot of Packages pane Screenshot of Packages pane

Long and wide dataframe layouts mainly affect readability. You may find that visually you may prefer the “wide” format, since you can see more of the data on the screen. However, all of the R functions we have used thus far expect for your data to be in a “long” data format. This is because the long format is more machine readable and is closer to the formatting of databases.

Questions which warrant different data formats

In interviews, each row contains the values of variables associated with each record (the unit), values such as the village of the respondent, the number of household members, or the type of wall their house had. This format allows for us to make comparisons across individual surveys, but what if we wanted to look at differences in households grouped by different types of items owned?

To facilitate this comparison we would need to create a new table where each row (the unit) was comprised of values of variables associated with items owned (i.e., items_owned). In practical terms this means the values of the items in items_owned (e.g. bicycle, radio, table, etc.) would become the names of column variables and the cells would contain values of TRUE or FALSE, for whether that household had that item.

Once we we’ve created this new table, we can explore the relationship within and between villages. The key point here is that we are still following a tidy data structure, but we have reshaped the data according to the observations of interest.

Alternatively, if the interview dates were spread across multiple columns, and we were interested in visualizing, within each village, how irrigation conflicts have changed over time. This would require for the interview date to be included in a single column rather than spread across multiple columns. Thus, we would need to transform the column names into values of a variable.

We can do both of these transformations with two tidyr functions, pivot_wider() and pivot_longer().

Pivoting wider

pivot_wider() takes three principal arguments:

  1. the data
  2. the names_from column variable whose values will become new column names.
  3. the values_from column variable whose values will fill the new column variables.

Further arguments include values_fill which, if set, fills in missing values with the value provided.

Let’s use pivot_wider() to transform interviews to create new columns for each item owned by a household. There are a couple of new concepts in this transformation, so let’s walk through it line by line. First we create a new object (interviews_items_owned) based on the interviews data frame.

interviews_items_owned <- interviews %>%

Then we will actually need to make our data frame longer, because we have multiple items in a single cell. We will use a new function, separate_longer_delim(), from the tidyr package to separate the values of items_owned based on the presence of semi-colons (;). The values of this variable were multiple items separated by semi-colons, so this action creates a row for each item listed in a household’s possession. Thus, we end up with a long format version of the dataset, with multiple rows for each respondent. For example, if a respondent has a television and a solar panel, that respondent will now have two rows, one with “television” and the other with “solar panel” in the items_owned column.

separate_longer_delim(items_owned, delim = ";") %>%

After this transformation, you may notice that the items_owned column contains NA values. This is because some of the respondents did not own any of the items that was in the interviewer’s list. We can use the replace_na() function to change these NA values to something more meaningful. The replace_na() function expects for you to give it a list() of columns that you would like to replace the NA values in, and the value that you would like to replace the NAs. This ends up looking like this:

replace_na(list(items_owned = "no_listed_items")) %>%

Next, we create a new variable named items_owned_logical, which has one value (TRUE) for every row. This makes sense, since each item in every row was owned by that household. We are constructing this variable so that when we spread the items_owned across multiple columns, we can fill the values of those columns with logical values describing whether the household did (TRUE) or didn’t (FALSE) own that particular item.

mutate(items_owned_logical = TRUE) %>%

Two tables shown side-by-side. The first row of the left table is highlighted in blue, and the first four rows of the right table are also highlighted in blue to show how each of the values of 'items owned' are given their own row with the separate longer delim function. The 'items owned logical' column is highlighted in yellow on the right table to show how the mutate function adds a new column.

Lastly, we use pivot_wider() to switch from long format to wide format. This creates a new column for each of the unique values in the items_owned column, and fills those columns with the values of items_owned_logical. We also declare that for items that are missing, we want to fill those cells with the value of FALSE instead of NA.

pivot_wider(names_from = items_owned,
            values_from = items_owned_logical,
            values_fill = list(items_owned_logical = FALSE))

Two tables shown side-by-side. The 'items owned' column is highlighted in blue on the left table, and the column names are highlighted in blue on the right table to show how the values of the 'items owned' become the column names in the output of the pivot wider function. The 'items owned logical' column is highlighted in yellow on the left table, and the values of the bicycle, television, and solar panel columns are highlighted in yellow on the right table to show how the values of the 'items owned logical' column became the values of all three of the aforementioned columns.

Combining the above steps, the chunk looks like this:

interviews_items_owned <- interviews %>%
  separate_longer_delim(items_owned, delim = ";") %>%
  replace_na(list(items_owned = "no_listed_items")) %>%
  mutate(items_owned_logical = TRUE) %>%
  pivot_wider(names_from = items_owned,
              values_from = items_owned_logical,
              values_fill = list(items_owned_logical = FALSE))

View the interviews_items_owned data frame. It should have 131 rows (the same number of rows you had originally), but extra columns for each item. How many columns were added? Notice that there is no longer a column titled items_owned. This is because there is a default parameter in pivot_wider() that drops the original column. The values that were in that column have now become columns named television, solar_panel, table, etc. You can use dim(interviews) and dim(interviews_wide) to see how the number of columns has changed between the two datasets.

This format of the data allows us to do interesting things, like make a table showing the number of respondents in each village who owned a particular item:

interviews_items_owned %>%
  filter(bicycle) %>%
  group_by(village) %>%
  count(bicycle)
## # A tibble: 3 × 3
## # Groups:   village [3]
##   village  bicycle     n
##   <chr>    <lgl>   <int>
## 1 Chirodzo TRUE       17
## 2 God      TRUE       23
## 3 Ruaca    TRUE       20

Or below we calculate the average number of items from the list owned by respondents in each village. This code uses the rowSums() function to count the number of TRUE values in the bicycle to car columns for each row, hence its name. Note that we replaced NA values with the value no_listed_items, so we must exclude this value in the aggregation. We then group the data by villages and calculate the mean number of items, so each average is grouped by village.

interviews_items_owned %>%
    select(-no_listed_items) %>% 
    mutate(number_items = rowSums(select(., bicycle:car))) %>%
    group_by(village) %>%
    summarize(mean_items = mean(number_items))
## # A tibble: 3 × 2
##   village  mean_items
##   <chr>         <dbl>
## 1 Chirodzo       4.54
## 2 God            3.98
## 3 Ruaca          5.57

Pivoting longer

The opposing situation could occur if we had been provided with data in the form of interviews_wide, where the items owned are column names, but we wish to treat them as values of an items_owned variable instead.

In this situation we are gathering these columns turning them into a pair of new variables. One variable includes the column names as values, and the other variable contains the values in each cell previously associated with the column names. We will do this in two steps to make this process a bit clearer.

pivot_longer() takes four principal arguments:

  1. the data
  2. cols are the names of the columns we use to fill the a new values variable (or to drop).
  3. the names_to column variable we wish to create from the cols provided.
  4. the values_to column variable we wish to create and fill with values associated with the cols provided.
interviews_long <- interviews_items_owned %>%
  pivot_longer(cols = bicycle:car,
               names_to = "items_owned",
               values_to = "items_owned_logical")

View both interviews_long and interviews_items_owned and compare their structure.

Exercise

We created some summary tables on interviews_items_owned using count and summarise. We can create the same tables on interviews_long, but this will require a different process.

  1. Make a table showing showing the number of respondents in each village who owned a particular item, and include all items. The difference between this format and the wide format is that you can now count all the items using the items_owned variable.

Solution

interviews_long %>%
  filter(items_owned_logical) %>% 
  group_by(village) %>% 
  count(items_owned)
## # A tibble: 47 × 3
## # Groups:   village [3]
##    village  items_owned         n
##    <chr>    <chr>           <int>
##  1 Chirodzo bicycle            17
##  2 Chirodzo computer            2
##  3 Chirodzo cow_cart            6
##  4 Chirodzo cow_plough         20
##  5 Chirodzo electricity         1
##  6 Chirodzo fridge              1
##  7 Chirodzo lorry               1
##  8 Chirodzo mobile_phone       25
##  9 Chirodzo motorcyle          13
## 10 Chirodzo no_listed_items     3
## # ℹ 37 more rows
  1. Calculate the average number of items from the list owned by respondents in each village. If you remove rows where items_owned_logical is FALSE you will have a data frame where the number of rows per household is equal to the number of items owned. You can use that to calculate the mean number of items per village.

Remember, you need to make sure we don’t count no_listed_items, since this is not an actual item, but rather the absence thereof.

Solution

interviews_long %>% 
  filter(items_owned_logical,
         items_owned != "no_listed_items") %>% 
  # to keep information per household, we count key_ID
  count(key_ID, village) %>% # we want to also keep the village variable
  group_by(village) %>% 
  summarise(mean_items = mean(n))
## # A tibble: 3 × 2
##   village  mean_items
##   <chr>         <dbl>
## 1 Chirodzo       4.92
## 2 God            4.38
## 3 Ruaca          5.93

Applying what we learned to clean our data

Now we have simultaneously learned about pivot_longer() and pivot_wider(), and fixed a problem in the way our data is structured. In the spreadsheets lesson, we learned that it’s best practice to have only a single piece of information in each cell of your spreadsheet. In this dataset, we have another column that stores multiple values in a single cell. Some of the cells in the months_lack_food column contain multiple months which, as before, are separated by semi-colons (;).

To create a data frame where each of the columns contain only one value per cell, we can repeat the steps we applied to items_owned and apply them to months_lack_food. Since we will be using this data frame for the next episode, we will call it interviews_plotting.

interviews_plotting <- interviews %>%
  ## pivot wider by items_owned
  separate_longer_delim(items_owned, delim = ";") %>%
  ## if there were no items listed, changing NA to no_listed_items
  replace_na(list(items_owned = "no_listed_items")) %>%
  mutate(items_owned_logical = TRUE) %>%
  pivot_wider(names_from = items_owned,
              values_from = items_owned_logical,
              values_fill = list(items_owned_logical = FALSE)) %>%
  ## pivot wider by months_lack_food
  separate_longer_delim(months_lack_food, delim = ";") %>%
  mutate(months_lack_food_logical = TRUE) %>%
  pivot_wider(names_from = months_lack_food,
              values_from = months_lack_food_logical,
              values_fill = list(months_lack_food_logical = FALSE)) %>%
  ## add some summary columns
  mutate(number_months_lack_food = rowSums(select(., Jan:May))) %>%
  mutate(number_items = rowSums(select(., bicycle:car)))

Exporting data

Now that you have learned how to use dplyr and tidyr to wrangle your raw data, you may want to export these new datasets to share them with your collaborators or for archival purposes.

Similar to the read_csv() function used for reading CSV files into R, there is a write_csv() function that generates CSV files from data frames.

Before using write_csv(), we are going to create a new folder, data_output, in our working directory that will store this generated dataset. We don’t want to write generated datasets in the same directory as our raw data. It’s good practice to keep them separate. The data folder should only contain the raw, unaltered data, and should be left alone to make sure we don’t delete or modify it. In contrast, our script will generate the contents of the data_output directory, so even if the files it contains are deleted, we can always re-generate them.

In preparation for our next lesson on plotting, we created a version of the dataset where each of the columns includes only one data value. Now we can save this data frame to our data_output directory.

write_csv(interviews_plotting, file = "data_output/interviews_plotting.csv")

keypoints

  • Use the tidyr package to change the layout of data frames.
  • Use pivot_wider() to go from long to wide format.
  • Use pivot_longer() to go from wide to long format.

Data Visualisation with ggplot2

  • This episode is a broad overview of ggplot2 and focuses on (1) getting familiar with the layering system of ggplot2, (2) using the argument group in the aes() function, (3) basic customization of the plots.
  • The episode depends on data created in the Data Wrangling with tidyr episode. If you did not get to or through all of the tidyr episode, you can have the learners access the data by either downloading it or quickly creating it using the tidyr code below. You will probably want to copy the code into the Etherpad.
  • If you did skip the tidyr episode, you might want to go over the exporting data section in that episode.

objectives

  • Produce scatter plots, boxplots, and barplots using ggplot.
  • Set universal plot settings.
  • Describe what faceting is and apply faceting in ggplot.
  • Modify the aesthetics of an existing ggplot plot (including axis labels and colour).
  • Build complex and customized plots from data in a data frame.
  • Recognize the differences between base R, lattice, and ggplot visualizations.

questions

  • What are the components of a ggplot?
  • What are the main differences between R base plots, lattice, and ggplot?
  • How do I create scatterplots, boxplots, and barplots?
  • How can I change the aesthetics (ex. colour, transparency) of my plot?
  • How can I create multiple plots at once?

We start by loading the required package. ggplot2 is also included in the tidyverse package.

If not still in the workspace, load the data we saved in the previous lesson.

interviews_plotting <- read_csv("data_output/interviews_plotting.csv")
## Rows: 131 Columns: 45
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (5): village, respondent_wall_type, memb_assoc, affect_conflicts, inst...
## dbl   (8): key_ID, no_membrs, years_liv, rooms, liv_count, no_meals, number_...
## lgl  (31): bicycle, television, solar_panel, table, cow_cart, radio, cow_plo...
## dttm  (1): interview_date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

If you were unable to complete the previous lesson or did not save the data, then you can create it now. Either download it using read_csv() (Option 1) or create it with the dplyr and tidyr code (Option 2).

Option 1: Download the data

interviews_plotting <- read_csv("https://raw.githubusercontent.com/datacarpentry/r-socialsci/main/episodes/data/interviews_plotting.csv")

Option 2: Create the data

## Can be used to load in data from previous lesson!
interviews_plotting <- interviews %>%
  ## pivot wider by items_owned
  separate_longer_delim(items_owned, delim = ";") %>%
  ## if there were no items listed, changing NA to no_listed_items
  replace_na(list(items_owned = "no_listed_items")) %>%
  mutate(items_owned_logical = TRUE) %>%
  pivot_wider(names_from = items_owned,
              values_from = items_owned_logical,
              values_fill = list(items_owned_logical = FALSE)) %>%
  ## pivot wider by months_lack_food
  separate_rows(months_lack_food, sep = ";") %>%
  mutate(months_lack_food_logical = TRUE) %>%
  pivot_wider(names_from = months_lack_food,
              values_from = months_lack_food_logical,
              values_fill = list(months_lack_food_logical = FALSE)) %>%
  ## add some summary columns
  mutate(number_months_lack_food = rowSums(select(., Jan:May))) %>%
  mutate(number_items = rowSums(select(., bicycle:car)))

Visualization Options in R

Before we start with ggplot2, it’s helpful to know that there are several ways to create visualizations in R. While ggplot2 is great for building complex and highly customizable plots, there are simpler and quicker alternatives that you might encounter or use depending on the context. Let’s briefly explore a few of them:

R Base Plots

Base R plots are the simplest form of visualization and are great for quick, exploratory analysis. You can create plots with very little code, but customizing them can be cumbersome compared to ggplot2.

Example of a simple scatterplot in base R using the no_membrs and liv_count variables:

plot(interviews_plotting$no_membrs, interviews_plotting$liv_count,
     main = "Base R Scatterplot",
     xlab = "Number of Household Members",
     ylab = "Number of Livestock Owned")

Lattice

Lattice is another plotting system in R, which allows for creating multi-panel plots easily. It’s different from ggplot2 because you define the entire plot in a single function call, and modifications after plotting are limited.

Example of a lattice plot using no_membrs and liv_count split by village:

library(lattice)
xyplot(liv_count ~ no_membrs | village, data = interviews_plotting,
       main = "Lattice Plot: Livestock Count by Household Members",
       xlab = "Number of Household Members",
       ylab = "Number of Livestock Owned")

Plotting with ggplot2

ggplot2 is a plotting package that makes it simple to create complex plots from data stored in a data frame. It provides a programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties. Therefore, we only need minimal changes if the underlying data change or if we decide to change from a bar plot to a scatterplot. This helps in creating publication quality plots with minimal amounts of adjustments and tweaking.

ggplot2 functions work best with data in the ‘long’ format, i.e., a column for every dimension, and a row for every observation. Well-structured data will save you lots of time when making figures with ggplot2

ggplot graphics are built step by step by adding new elements. Adding layers in this fashion allows for extensive flexibility and customization of plots.

Each chart built with ggplot2 must include the following

Thus, the template for graphic in ggplot2 is:

<DATA> %>%
    ggplot(aes(<MAPPINGS>)) +
    <GEOM_FUNCTION>()

Remember from the last lesson that the pipe operator %>% places the result of the previous line(s) into the first argument of the function. ggplot is a function that expects a data frame to be the first argument. This allows for us to change from specifying the data = argument within the ggplot function and instead pipe the data into the function.

interviews_plotting %>%
    ggplot()
interviews_plotting %>%
    ggplot(aes(x = no_membrs, y = number_items))

To add a geom to the plot use the + operator. Because we have two continuous variables, let’s use geom_point() first:

interviews_plotting %>%
    ggplot(aes(x = no_membrs, y = number_items)) +
    geom_point()

The + in the ggplot2 package is particularly useful because it allows you to modify existing ggplot objects. This means you can easily set up plot templates and conveniently explore different types of plots, so the above plot can also be generated with code like this, similar to the “intermediate steps” approach in the previous lesson:

# Assign plot to a variable
interviews_plot <- interviews_plotting %>%
    ggplot(aes(x = no_membrs, y = number_items))

# Draw the plot as a dot plot
interviews_plot +
    geom_point()

Notes

## This is the correct syntax for adding layers
interviews_plot +
    geom_point()

## This will not add the new layer and will return an error message
interviews_plot
+ geom_point()

Building your plots iteratively

Building plots with ggplot2 is typically an iterative process. We start by defining the dataset we’ll use, lay out the axes, and choose a geom:

interviews_plotting %>%
    ggplot(aes(x = no_membrs, y = number_items)) +
    geom_point()

Scatter plot of number of items owned versus number of household members.

Then, we start modifying this plot to extract more information from it. For instance, when inspecting the plot we notice that points only appear at the intersection of whole numbers of no_membrs and number_items. Also, from a rough estimate, it looks like there are far fewer dots on the plot than there rows in our dataframe. This should lead us to believe that there may be multiple observations plotted on top of each other (e.g. three observations where no_membrs is 3 and number_items is 1).

There are two main ways to alleviate overplotting issues:

  1. changing the transparency of the points
  2. jittering the location of the points

Let’s first explore option 1, changing the transparency of the points. What we mean when we say “transparency” we mean the opacity of point, or your ability to see through the point. We can control the transparency of the points with the alpha argument to geom_point. Values of alpha range from 0 to 1, with lower values corresponding to more transparent colors (an alpha of 1 is the default value). Specifically, an alpha of 0.1, would make a point one-tenth as opaque as a normal point. Stated differently ten points stacked on top of each other would correspond to a normal point.

Here, we change the alpha to 0.5, in an attempt to help fix the overplotting. While the overplotting isn’t solved, adding transparency begins to address this problem, as the points where there are overlapping observations are darker (as opposed to lighter gray):

interviews_plotting %>%
    ggplot(aes(x = no_membrs, y = number_items)) +
    geom_point(alpha = 0.3)

Scatter plot of number of items owned versus number of household members, with transparency added to points.

That only helped a little bit with the overplotting problem, so let’s try option two. We can jitter the points on the plot, so that we can see each point in the locations where there are overlapping points. Jittering introduces a little bit of randomness into the position of our points. You can think of this process as taking the overplotted graph and giving it a tiny shake. The points will move a little bit side-to-side and up-and-down, but their position from the original plot won’t dramatically change. Note that this solution is suitable for plotting integer figures, while for numeric figures with decimals, geom_jitter() becomes inappropriate because it obscures the true value of the observation.

We can jitter our points using the geom_jitter() function instead of the geom_point() function, as seen below:

interviews_plotting %>%
    ggplot(aes(x = no_membrs, y = number_items)) +
    geom_jitter()

Scatter plot of number of items owned versus number of household members, showing jitter.

The geom_jitter() function allows for us to specify the amount of random motion in the jitter, using the width and height arguments. When we don’t specify values for width and height, geom_jitter() defaults to 40% of the resolution of the data (the smallest change that can be measured). Hence, if we would like less spread in our jitter than was default, we should pick values between 0.1 and 0.4. Experiment with the values to see how your plot changes.

interviews_plotting %>%
    ggplot(aes(x = no_membrs, y = number_items)) +
    geom_jitter(alpha = 0.3,
                width = 0.2,
                height = 0.2)

Scatter plot of number of items owned versus number of household members, with jitter and transparency.

For our final change, we can also add colours for all the points by specifying a color argument inside the geom_jitter() function:

interviews_plotting %>%
    ggplot(aes(x = no_membrs, y = number_items)) +
    geom_jitter(alpha = 0.3,
                color = "blue",
                width = 0.2,
                height = 0.2)

Scatter plot of number of items owned versus number of household members, showing points as blue.

To colour each village in the plot differently, you could use a vector as an input to the argument color. However, because we are now mapping features of the data to a colour, instead of setting one colour for all points, the colour of the points now needs to be set inside a call to the aes function. When we map a variable in our data to the colour of the points, ggplot2 will provide a different colour corresponding to the different values of the variable. We will continue to specify the value of alpha, width, and height outside of the aes function because we are using the same value for every point. ggplot2 understands both the Commonwealth English and American English spellings for colour, i.e., you can use either color or colour. Here is an example where we color points by the village of the observation:

interviews_plotting %>%
    ggplot(aes(x = no_membrs, y = number_items)) +
    geom_jitter(aes(color = village), alpha = 0.3, width = 0.2, height = 0.2)

There appears to be a positive trend between number of household members and number of items owned (from the list provided). Additionally, this trend does not appear to be different by village.

Notes

As you will learn, there are multiple ways to plot the a relationship between variables. Another way to plot data with overlapping points is to use the geom_count plotting function. The geom_count() function makes the size of each point representative of the number of data items of that type and the legend gives point sizes associated to particular numbers of items.

interviews_plotting %>%
   ggplot(aes(x = no_membrs, y = number_items, color = village)) +
   geom_count()

Previous plot with dots colored by village.

Exercise

Use what you just learned to create a scatter plot of rooms by village with the respondent_wall_type showing in different colours. Does this seem like a good way to display the relationship between these variables? What other kinds of plots might you use to show this type of data?

::::::::::::::: solution

Solution

interviews_plotting %>%
    ggplot(aes(x = village, y = rooms)) +
    geom_jitter(aes(color = respondent_wall_type),
        alpha = 0.3,
            width = 0.2,
            height = 0.2)

Scatter plot showing positive trend between number of household members and number of items owned.

This is not a great way to show this type of data because it is difficult to distinguish between villages. What other plot types could help you visualize this relationship better?

Boxplot

We can use boxplots to visualize the distribution of rooms for each wall type:

interviews_plotting %>%
    ggplot(aes(x = respondent_wall_type, y = rooms)) +
    geom_boxplot()

Box plot of number of rooms by wall type.

By adding points to a boxplot, we can have a better idea of the number of measurements and of their distribution:

interviews_plotting %>%
    ggplot(aes(x = respondent_wall_type, y = rooms)) +
    geom_boxplot(alpha = 0) +
    geom_jitter(alpha = 0.3,
            color = "tomato",
            width = 0.2,
            height = 0.2)

Previous plot with dot plot added as additional layer to show individual values. Boxplot layer is transparent.

We can see that muddaub houses and sunbrick houses tend to be smaller than burntbrick houses.

Notice how the boxplot layer is behind the jitter layer? What do you need to change in the code to put the boxplot layer in front of the jitter layer?

Exercise

Boxplots are useful summaries, but hide the shape of the distribution. For example, if the distribution is bimodal, we would not see it in a boxplot. An alternative to the boxplot is the violin plot, where the shape (of the density of points) is drawn.

  • Replace the box plot with a violin plot; see geom_violin().

Solution

interviews_plotting %>%
  ggplot(aes(x = respondent_wall_type, y = rooms)) +
  geom_violin(alpha = 0) +
  geom_jitter(alpha = 0.5, color = "tomato")
## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.

So far, we’ve looked at the distribution of room number within wall type. Try making a new plot to explore the distribution of another variable within wall type.

Solution

interviews_plotting %>%
   ggplot(aes(x = respondent_wall_type, y = liv_count)) +
   geom_boxplot(alpha = 0) +
   geom_jitter(alpha = 0.5, width = 0.2, height = 0.2)

Box plot of number of livestock owned by wall type, with dot plot added as additional layer to show individual values.

Solution

interviews_plotting %>%
  ggplot(aes(x = respondent_wall_type, y = liv_count)) +
  geom_boxplot(alpha = 0) +
  geom_jitter(aes(color = memb_assoc), alpha = 0.5, width = 0.2, height = 0.2)

Previous plot with dots colored based on whether respondent was a member of an irrigation association.

Barplots

Barplots are also useful for visualizing categorical data. By default, geom_bar accepts a variable for x, and plots the number of instances each value of x (in this case, wall type) appears in the dataset.

interviews_plotting %>%
    ggplot(aes(x = respondent_wall_type)) +
    geom_bar()

Bar plot showing counts of respondent wall types.

We can use the fill aesthetic for the geom_bar() geom to colour bars by the portion of each count that is from each village.

interviews_plotting %>%
    ggplot(aes(x = respondent_wall_type)) +
    geom_bar(aes(fill = village))

Stacked bar plot of wall types showing each village as a different color.

This creates a stacked bar chart. These are generally more difficult to read than side-by-side bars. We can separate the portions of the stacked bar that correspond to each village and put them side-by-side by using the position argument for geom_bar() and setting it to “dodge”.

interviews_plotting %>%
    ggplot(aes(x = respondent_wall_type)) +
    geom_bar(aes(fill = village), position = "dodge")

Bar plot of respondent wall types with each village as a separate bar.

This is a nicer graphic, but we’re more likely to be interested in the proportion of each housing type in each village than in the actual count of number of houses of each type (because we might have sampled different numbers of households in each village). To compare proportions, we will first create a new data frame (percent_wall_type) with a new column named “percent” representing the percent of each house type in each village. We will remove houses with cement walls, as there was only one in the dataset.

percent_wall_type <- interviews_plotting %>%
    filter(respondent_wall_type != "cement") %>%
    count(village, respondent_wall_type) %>%
    group_by(village) %>%
    mutate(percent = (n / sum(n)) * 100) %>%
    ungroup()

Now we can use this new data frame to create our plot showing the percentage of each house type in each village.

percent_wall_type %>%
    ggplot(aes(x = village, y = percent, fill = respondent_wall_type)) +
    geom_bar(stat = "identity", position = "dodge")

Side by side bar plot showing percent of respondents in each village with each wall type.

Exercise

Create a bar plot showing the proportion of respondents in each village who are or are not part of an irrigation association (memb_assoc). Include only respondents who answered that question in the calculations and plot. Which village had the lowest proportion of respondents in an irrigation association?

Solution

percent_memb_assoc <- interviews_plotting %>%
  filter(!is.na(memb_assoc)) %>%
  count(village, memb_assoc) %>%
  group_by(village) %>%
  mutate(percent = (n / sum(n)) * 100) %>%
  ungroup()

percent_memb_assoc %>%
   ggplot(aes(x = village, y = percent, fill = memb_assoc)) +
    geom_bar(stat = "identity", position = "dodge")

Bar plot showing percent of respondents in each village who were part of association.

Ruaca had the lowest proportion of members in an irrigation association.

Adding Labels and Titles

By default, the axes labels on a plot are determined by the name of the variable being plotted. However, ggplot2 offers lots of customization options, like specifying the axes labels, and adding a title to the plot with relatively few lines of code. We will add more informative x-and y-axis labels to our plot, a more explanatory label to the legend, and a plot title.

The labs function takes the following arguments:

percent_wall_type %>%
    ggplot(aes(x = village, y = percent, fill = respondent_wall_type)) +
    geom_bar(stat = "identity", position = "dodge") +
    labs(title = "Proportion of wall type by village",
         fill = "Type of Wall in Home",
         x = "Village",
         y = "Percent")

Previous plot with plot title and labells added.

Faceting

Rather than creating a single plot with side-by-side bars for each village, we may want to create multiple plot, where each plot shows the data for a single village. This would be especially useful if we had a large number of villages that we had sampled, as a large number of side-by-side bars will become more difficult to read.

ggplot2 has a special technique called faceting that allows the user to split one plot into multiple plots based on a factor included in the dataset. We will use it to split our barplot of housing type proportion by village so that each village has its own panel in a multi-panel plot:

percent_wall_type %>%
    ggplot(aes(x = respondent_wall_type, y = percent)) +
    geom_bar(stat = "identity", position = "dodge") +
    labs(title="Proportion of wall type by village",
         x="Wall Type",
         y="Percent") +
    facet_wrap(~ village)

Bar plot showing percent of each wall type in each village.

Click the “Zoom” button in your RStudio plots pane to view a larger version of this plot.

Usually plots with white background look more readable when printed. We can set the background to white using the function theme_bw(). Additionally, you can remove the grid:

percent_wall_type %>%
    ggplot(aes(x = respondent_wall_type, y = percent)) +
    geom_bar(stat = "identity", position = "dodge") +
    labs(title="Proportion of wall type by village",
         x="Wall Type",
         y="Percent") +
    facet_wrap(~ village) +
    theme_bw() +
    theme(panel.grid = element_blank())

Bar plot showing percent of each wall type in each village, with black and white theme applied.

What if we wanted to see the proportion of respondents in each village who owned a particular item? We can calculate the percent of people in each village who own each item and then create a faceted series of bar plots where each plot is a particular item. First we need to calculate the percentage of people in each village who own each item:

percent_items <- interviews_plotting %>%
    group_by(village) %>%
    summarize(across(bicycle:no_listed_items, ~ sum(.x) / n() * 100)) %>%
    pivot_longer(bicycle:no_listed_items, names_to = "items", values_to = "percent")

To calculate this percentage data frame, we needed to use the across() function within a summarize() operation. Unlike the previous example with a single wall type variable, where each response was exactly one of the types specified, people can (and do) own more than one item. So there are multiple columns of data (one for each item), and the percentage calculation needs to be repeated for each column.

Combining summarize() with across() allows us to specify first, the columns to be summarized (bicycle:no_listed_items) and then the calculation. Because our calculation is a bit more complex than is available in a built-in function, we define a new formula:

After the summarize() operation, we have a table of percentages with each item in its own column, so a pivot_longer() is required to transform the table into an easier format for plotting. Using this data frame, we can now create a multi-paneled bar plot.

percent_items %>%
    ggplot(aes(x = village, y = percent)) +
    geom_bar(stat = "identity", position = "dodge") +
    facet_wrap(~ items) +
    theme_bw() +
    theme(panel.grid = element_blank())

Multi-panel bar chart showing percent of respondents in each village and who owned each item, with no grids behid bars.

ggplot2 themes

In addition to theme_bw(), which changes the plot background to white, ggplot2 comes with several other themes which can be useful to quickly change the look of your visualization. The complete list of themes is available at https://ggplot2.tidyverse.org/reference/ggtheme.html. theme_minimal() and theme_light() are popular, and theme_void() can be useful as a starting point to create a new hand-crafted theme.

The ggthemes package provides a wide variety of options (including an Excel 2003 theme). The ggplot2 extensions website provides a list of packages that extend the capabilities of ggplot2, including additional themes.

Exercise

Experiment with at least two different themes. Build the previous plot using each of those themes. Which do you like best?

Customization

Take a look at the ggplot2 cheat sheet, and think of ways you could improve the plot.

Now, let’s change names of axes to something more informative than ‘village’ and ‘percent’ and add a title to the figure:

percent_items %>%
    ggplot(aes(x = village, y = percent)) +
    geom_bar(stat = "identity", position = "dodge") +
    facet_wrap(~ items) +
    labs(title = "Percent of respondents in each village who owned each item",
         x = "Village",
         y = "Percent of Respondents") +
    theme_bw()

The axes have more informative names, but their readability can be improved by increasing the font size:

percent_items %>%
    ggplot(aes(x = village, y = percent)) +
    geom_bar(stat = "identity", position = "dodge") +
    facet_wrap(~ items) +
    labs(title = "Percent of respondents in each village who owned each item",
         x = "Village",
         y = "Percent of Respondents") +
    theme_bw() +
    theme(text = element_text(size = 16))

Note that it is also possible to change the fonts of your plots. If you are on Windows, you may have to install the extrafont package, and follow the instructions included in the README for this package.

After our manipulations, you may notice that the values on the x-axis are still not properly readable. Let’s change the orientation of the labels and adjust them vertically and horizontally so they don’t overlap. You can use a 90-degree angle, or experiment to find the appropriate angle for diagonally oriented labels. With a larger font, the title also runs off. We can add “\n” in the string for the title to insert a new line:

percent_items %>%
    ggplot(aes(x = village, y = percent)) +
    geom_bar(stat = "identity", position = "dodge") +
    facet_wrap(~ items) +
    labs(title = "Percent of respondents in each village \n who owned each item",
         x = "Village",
         y = "Percent of Respondents") +
    theme_bw() +
    theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 45,
                                     hjust = 0.5, vjust = 0.5),
          axis.text.y = element_text(colour = "grey20", size = 12),
          text = element_text(size = 16))

Multi-panel bar charts showing percent of respondents in each village and who owned each item, with grids behind the bars.

If you like the changes you created better than the default theme, you can save them as an object to be able to easily apply them to other plots you may create. We can also add plot.title = element_text(hjust = 0.5) to centre the title:

grey_theme <- theme(axis.text.x = element_text(colour = "grey20", size = 12,
                                               angle = 45, hjust = 0.5,
                                               vjust = 0.5),
                    axis.text.y = element_text(colour = "grey20", size = 12),
                    text = element_text(size = 16),
                    plot.title = element_text(hjust = 0.5))


percent_items %>%
    ggplot(aes(x = village, y = percent)) +
    geom_bar(stat = "identity", position = "dodge") +
    facet_wrap(~ items) +
    labs(title = "Percent of respondents in each village \n who owned each item",
         x = "Village",
         y = "Percent of Respondents") +
    grey_theme

Exercise

With all of this information in hand, please take another five minutes to either improve one of the plots generated in this exercise or create a beautiful graph of your own. Use the RStudio ggplot2 cheat sheet for inspiration. Here are some ideas:

After creating your plot, you can save it to a file in your favourite format. The Export tab in the Plot pane in RStudio will save your plots at low resolution, which will not be accepted by many journals and will not scale well for posters.

Instead, use the ggsave() function, which allows you to easily change the dimension and resolution of your plot by adjusting the appropriate arguments (width, height and dpi).

Make sure you have the fig_output/ folder in your working directory.

my_plot <- percent_items %>%
    ggplot(aes(x = village, y = percent)) +
    geom_bar(stat = "identity", position = "dodge") +
    facet_wrap(~ items) +
    labs(title = "Percent of respondents in each village \n who owned each item",
         x = "Village",
         y = "Percent of Respondents") +
    theme_bw() +
    theme(axis.text.x = element_text(color = "grey20", size = 12, angle = 45,
                                     hjust = 0.5, vjust = 0.5),
          axis.text.y = element_text(color = "grey20", size = 12),
          text = element_text(size = 16),
          plot.title = element_text(hjust = 0.5))

ggsave("fig_output/name_of_file.png", my_plot, width = 15, height = 10)

Note: The parameters width and height also determine the font size in the saved plot.

keypoints

Thank you